└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Deep-Learning-Based-Anomaly-Detection 2 | 3 | ***Anomaly Detection***: The process of detectingdata instances that ***significantly deviate*** from the majority of the whole dataset. 4 | 5 | Contributed by Chunyang Zhang. 6 | 7 | ## 🤝 Looking for Collaborators | 寻找协作者 8 | 9 | This is an open-source repository for **Deep-Learning-Based Anomaly Detection**, focused on collecting and organizing literature and resources related to **anomaly detection using deep learning techniques**. Anomaly detection refers to the process of identifying data points that significantly differ from the majority of a dataset. 10 | 11 | I am currently seeking collaborators to **help maintain and update** this repository. Contributions can include adding new papers or benchmarks, refining structure, or tagging key methods. I will **check the repository weekly**, but the project will be **mainly updated by collaborators** going forward. 12 | 13 | If you are interested in deep learning for anomaly detection, feel free to submit a PR, open an issue, or contact me directly: **bitzhangcy@gmail.com**. 14 | 15 | 这是一个关于**基于深度学习的异常检测（Anomaly Detection）**的开源文献与资源整理仓库，旨在系统性地收录该领域的最新研究。异常检测指的是识别那些在数据集中**显著偏离多数样本的实例**。 16 | 17 | 我目前正在寻找对该方向感兴趣的朋友，**一同维护和更新**这个仓库。欢迎协作者参与添加新论文、新数据集、优化结构或标注关键方法等。后续更新将**主要由协作者主导**，我本人会**每周关注一次仓库进展**。 18 | 19 | 如果你对深度学习异常检测感兴趣，欢迎通过 PR、issue 或直接邮件联系我：**bitzhangcy@gmail.com** 20 | 21 | 22 | ## [Content](#content) 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 |
1. Survey
2. Methodology
 2.1 AutoEncoder 2.2 GAN
 2.3 Flow 2.4 Diffusion Model
 2.5 Transformer 2.6 Convolution
 2.7 GNN 2.8 Time Series
 2.9 Tabular 2.10 Large Model
 2.11 Out of Distribution 2.12 Reinforcement Learning
 2.13 In-Context Learning 2.14 Representation Learning
3. Mechanism
 3.1 Dataset 3.2 Benchmark
 3.3 Investigation 3.4 Domain Adaptation
 3.5 Loss Function 3.6 Model Selection
 3.7 Knowledge Distillation 3.8 Data Augmentation
 3.9 Outlier Exposure 3.10 Contrastive Learning
 3.11 Continual Learning 3.12 Multi Scale
 3.13 Statistics 3.14 Density Estimation
 3.15 Support Vector 3.16 Sparse Coding
 3.17 Energy Model 3.18 Memory Bank
 3.19 Cluster 3.20 Isolation
 3.21 Multi Modal 3.22 Optimal Transport
 3.23 Causal Inference 3.24 Gaussian Process
 3.25 Multi Task 3.26 Interpretability
 3.27 Neural Process 3.28 Nonparametric Approach
 3.29 Federated Learning 3.30 Online Learning
4. Application
 4.1 Finance 4.2 Point Cloud
 4.3 Autonomous Driving 4.4 Medical Image
 4.5 Robotics 4.6 Cyber Intrusion
 4.7 Diagnosis 4.8 High Performance Computing
 4.9 Physics 4.10 Industry Process
 4.11 Software 4.12 Astronomy
 4.11 Human 4.12 Climate
145 | 146 | 147 | 148 | 149 | ## [Survey](#content) 150 | 1. **A survey of single-scene video anomaly detection.** TPAMI, 2022. [paper](https://ieeexplore.ieee.org/document/9271895) 151 | 152 | *Bharathkumar Ramachandra, Michael J. Jones, and Ranga Raju Vatsavai.* 153 | 154 | 1. **Deep learning for anomaly detection: A review.** ACM Computing Surveys, 2022. [paper](https://dl.acm.org/doi/10.1145/3439950) 155 | 156 | *Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel.* 157 | 158 | 1. **A unifying review of deep and shallow anomaly detection.** Proceedings of the IEEE, 2020. [paper](https://ieeexplore.ieee.org/document/9347460) 159 | 160 | *Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, GrÉgoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, and Klaus-robert MÜller.* 161 | 162 | 1. **A review on outlier/anomaly detection in time series data.** ACM Computing Surveys, 2022. [paper](https://dl.acm.org/doi/10.1145/3444690) 163 | 164 | *Ane Blázquez-García, Angel Conde, Usue Mori, and Jose A. Lozano.* 165 | 166 | 1. **Anomaly detection in autonomous driving: A survey.** CVPR, 2022. [paper](https://openaccess.thecvf.com/content/CVPR2022W/WAD/html/Bogdoll_Anomaly_Detection_in_Autonomous_Driving_A_Survey_CVPRW_2022_paper.html) 167 | 168 | *Daniel Bogdoll, Maximilian Nitsche, and J. Marius Zöllner.* 169 | 170 | 1. **A comprehensive survey on graph anomaly detection with deep learning.** TKDE, 2021. [paper](https://ieeexplore.ieee.org/document/9565320) 171 | 172 | *Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, and Hui Xiong, and Leman Akoglu.* 173 | 174 | 1. **Transformers in time series: A survey.** arXiv, 2022. [paper](https://arxiv.org/abs/2202.07125) 175 | 176 | *Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun.* 177 | 178 | 1. **A survey on explainable anomaly detection.** arXiv, 2022. [paper](https://arxiv.org/abs/2210.06959) 179 | 180 | *Zhong Li, Yuxuan Zhu, and Matthijs van Leeuwen.* 181 | 182 | 1. **Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues.** KBS, 2020. [paper](https://www.sciencedirect.com/science/article/pii/S0950705119304897) 183 | 184 | *Arwa Aldweesh, Abdelouahid Derhab, and Ahmed Z.Emam.* 185 | 186 | 1. **Deep learning-based anomaly detection in cyber-physical systems: Progress and oportunities.** ACM Computing Surveys, 2022. [paper](https://dl.acm.org/doi/10.1145/3453155) 187 | 188 | *Yuan Luo, Ya Xiao, Long Cheng, Guojun Peng, and Danfeng (Daphne) Yao.* 189 | 190 | 1. **GAN-based anomaly detection: A review.** Neurocomputing, 2022. [paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231221019482) 191 | 192 | *Xuan Xia, Xizhou Pan, Nan Lia, Xing He, Lin Ma, Xiaoguang Zhang, and Ning Ding.* 193 | 194 | 1. **Unsupervised anomaly detection in time-series: An extensive evaluation and analysis of state-of-the-art methods.** arXiv, 2022. [paper](https://arxiv.org/abs/2212.03637) 195 | 196 | *Nesryne Mejri, Laura Lopez-Fuentes, Kankana Roy, Pavel Chernakov, Enjie Ghorbel, and Djamila Aouada.* 197 | 198 | 1. **Deep learning for time series anomaly detection: A survey.** arXiv, 2022. [paper](https://arxiv.org/abs/2211.05244) 199 | 200 | *Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, and Mahsa Salehi.* 201 | 202 | 1. **A survey of deep learning-based network anomaly detection.** Cluster Computing, 2019. [paper](https://link.springer.com/article/10.1007/s10586-017-1117-8) 203 | 204 | *Donghwoon Kwon, Hyunjoo Kim, Jinoh Kim, Sang C. Suh, Ikkyun Kim, and Kuinam J. Kim.* 205 | 206 | 1. **Survey on anomaly detection using data mining techniques.** Procedia Computer Science, 2015. [paper](https://www.sciencedirect.com/science/article/pii/S1877050915023479) 207 | 208 | *Shikha Agrawal and Jitendra Agrawal.* 209 | 210 | 1. **Graph based anomaly detection and description: A survey.** Data Mining and Knowledge Discovery, 2015. [paper](https://link.springer.com/article/10.1007/s10618-014-0365-y) 211 | 212 | *Leman Akoglu, Hanghang Tong, and Danai Koutra.* 213 | 214 | 1. **Domain anomaly detection in machine perception: A system architecture and taxonomy.** TPAMI, 2014. [paper](https://ieeexplore.ieee.org/document/6636290) 215 | 216 | *Josef Kittler, William Christmas, Teófilo de Campos, David Windridge, Fei Yan, John Illingworth, and Magda Osman.* 217 | 218 | 1. **Graph-based time-series anomaly detection: A Survey.** arXiv, 2023. [paper](https://arxiv.org/abs/2302.00058) 219 | 220 | *Thi Kieu Khanh Ho, Ali Karami, and Narges Armanfard.* 221 | 222 | 1. **Weakly supervised anomaly detection: A survey.** arXiv, 2023. [paper](https://arxiv.org/abs/2302.04549) 223 | 224 | *Minqi Jiang, Chaochuan Hou, Ao Zheng, Xiyang Hu, Songqiao Han, Hailiang Huang, Xiangnan He, Philip S. Yu, and Yue Zhao.* 225 | 226 | 1. **A comprehensive survey of deep transfer learning for anomaly detection in industrial time series: Methods, applications, and directions.** arXiv, 2023. [paper](https://arxiv.org/abs/2307.05638) 227 | 228 | *Peng Yan, Ahmed Abdulkadir, Matthias Rosenthal, Gerrit A. Schatte, Benjamin F. Grewe, and Thilo Stadelmann.* 229 | 230 | 1. **Survey on video anomaly detection in dynamic scenes with moving cameras.** arXiv, 2023. [paper](https://arxiv.org/abs/2308.07050) 231 | 232 | *Runyu Jiao, Yi Wan, Fabio Poiesi, and Yiming Wang.* 233 | 234 | 1. **Physics-informed machine learning for data anomaly detection, classification, localization, and mitigation: A review, challenges, and path forward.** arXiv, 2023. [paper](https://arxiv.org/abs/2309.10788) 235 | 236 | *Mehdi Jabbari Zideh, Paroma Chatterjee, and Anurag K. Srivastava.* 237 | 238 | 1. **Detecting and learning out-of-distribution data in the open world: Algorithm and theory.** Thesis, 2023. [Ph.D.](https://arxiv.org/abs/2310.06221) 239 | 240 | *Yiyou Sun.* 241 | 242 | 1. **Meta-survey on outlier and anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2312.07101) 243 | 244 | *Madalina Olteanu, Fabrice Rossi, and Florian Yger.* 245 | 246 | 1. **Anomaly detection in surveillance videos: A thematic taxonomy of deep models, review and performance analysis.** Artificial Intelligence Review, 2023. [paper](https://link.springer.com/article/10.1007/s10462-022-10258-6) 247 | 248 | *S. Chandrakala, K. Deepak, and G. Revathy.* 249 | 250 | 1. **Revisiting VAE for unsupervised time series anomaly detection: A frequency perspective.** WWW, 2024. [paper](https://arxiv.org/abs/2402.02820) 251 | 252 | *Zexin Wang, Changhua Pei, Minghua Ma, Xin Wang, Zhihan Li, Dan Pei, Saravan Rajmohan, Dongmei Zhang, Qingwei Lin, Haiming Zhang, Jianhui Li, and Gaogang Xie.* 253 | 254 | 1. **Can tree based approaches surpass deep learning in anomaly detection? A benchmarking study.** arXiv, 2024. [paper](https://arxiv.org/abs/2402.07281) 255 | 256 | *Santonu Sarkar, Shanay Mehta, Nicole Fernandes, Jyotirmoy Sarkar, and Snehanshu Saha.* 257 | 258 | 1. **Large language models for forecasting and anomaly detection: A systematic literature review.** arXiv, 2024. [paper](https://arxiv.org/abs/2402.10350) 259 | 260 | *Jing Su, Chufeng Jiang, Xin Jin, Yuxin Qiao, Tingsong Xiao, Hongda Ma, Rong Wei, Zhi Jing, Jiajun Xu, and Junhong Lin.* 261 | 262 | 1. **A survey of graph neural networks in real world: Imbalance, noise, privacy and OOD challenges.** arXiv, 2024. [paper](https://arxiv.org/abs/2403.04468) 263 | 264 | *Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Xiao Luo, Philip S. Yu, and Ming Zhang.* 265 | 266 | 1. **Anomaly detection in graph structured data: A survey.** arXiv, 2024. [paper](https://arxiv.org/abs/2405.06172) 267 | 268 | *Prabin B Lamichhane and William Eberle.* 269 | 270 | 1. **Deep learning for video anomaly detection: A review.** arXiv, 2024. [paper](https://arxiv.org/abs/2409.05383) 271 | 272 | *Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang, Peng Wang, and Yanning Zhang.* 273 | 274 | 275 | ## [Methodology](#content) 276 | ### [AutoEncoder](#content) 277 | 1. **Graph regularized autoencoder and its application in unsupervised anomaly detection.** TPAMI, 2022. [paper](https://ieeexplore.ieee.org/document/9380495) 278 | 279 | *Imtiaz Ahmed, Travis Galoppo, Xia Hu, and Yu Ding.* 280 | 281 | 1. **Innovations autoencoder and its application in one-class anomalous sequence detection.** JMLR, 2022. [paper](https://www.jmlr.org/papers/volume23/21-0735/21-0735.pdf) 282 | 283 | *Xinyi Wang and Lang Tong.* 284 | 285 | 1. **Autoencoders-A comparative analysis in the realm of anomaly detection.** CVPR, 2022. [paper](https://openaccess.thecvf.com/content/CVPR2022W/WiCV/html/Schneider_Autoencoders_-_A_Comparative_Analysis_in_the_Realm_of_Anomaly_CVPRW_2022_paper.html) 286 | 287 | *Sarah Schneider, Doris Antensteiner, Daniel Soukup, and Matthias Scheutz.* 288 | 289 | 1. **Attention guided anomaly localization in images.** ECCV, 2020. [paper](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_29) 290 | 291 | *Shashanka Venkataramanan, Kuan-Chuan Peng, Rajat Vikram Singh, and Abhijit Mahalanobis.* 292 | 293 | 1. **Latent space autoregression for novelty detection.** CVPR, 2018. [paper](https://openaccess.thecvf.com/content_CVPR_2019/html/Abati_Latent_Space_Autoregression_for_Novelty_Detection_CVPR_2019_paper.html) 294 | 295 | *Davide Abati, Angelo Porrello, Simone Calderara, and Rita Cucchiara.* 296 | 297 | 1. **Anomaly detection in time series with robust variational quasi-recurrent autoencoders.** ICDM, 2018. [paper](https://ieeexplore.ieee.org/abstract/document/9835268) 298 | 299 | *Tung Kieu, Bin Yang, Chenjuan Guo, Razvan-Gabriel Cirstea, Yan Zhao, Yale Song, and Christian S. Jensen.* 300 | 301 | 1. **Robust and explainable autoencoders for unsupervised time series outlier detection.** ICDE, 2022. [paper](https://ieeexplore.ieee.org/document/9835554) 302 | 303 | *Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen, Yan Zhao, Feiteng Huang, and Kai Zheng.* 304 | 305 | 1. **Latent feature learning via autoencoder training for automatic classification configuration recommendation.** KBS, 2022. [paper](https://www.sciencedirect.com/science/article/pii/S0950705122013144) 306 | 307 | *Liping Deng and Mingqing Xiao.* 308 | 309 | 1. **Deep autoencoding Gaussian mixture model for unsupervised anomaly detection.** ICLR, 2018. [paper](https://openreview.net/forum?id=BJJLHbb0-) 310 | 311 | *Bo Zongy, Qi Songz, Martin Renqiang Miny, Wei Chengy, Cristian Lumezanuy, Daeki Choy, and Haifeng Chen.* 312 | 313 | 1. **Anomaly detection with robust deep autoencoders.** KDD, 2017. [paper](https://dl.acm.org/doi/10.1145/3097983.3098052) 314 | 315 | *Chong Zhou and Randy C. Paffenroth.* 316 | 317 | 1. **Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications.** WWW, 2018. [paper](https://dl.acm.org/doi/abs/10.1145/3178876.3185996) 318 | 319 | *Haowen Xu, Wenxiao Chen, Nengwen Zhao,Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, Jie Chen, Zhaogang Wang, and Honglin Qiao.* 320 | 321 | 1. **Spatio-temporal autoencoder for video anomaly detection.** MM, 2017. [paper](https://dl.acm.org/doi/abs/10.1145/3123266.3123451) 322 | 323 | *Yiru Zhao, Bing Deng, Chen Shen, Yao Liu, Hongtao Lu, and Xiansheng Hua.* 324 | 325 | 1. **Learning discriminative reconstructions for unsupervised outlier removal.** ICCV, 2015. [paper](https://ieeexplore.ieee.org/document/7410534) 326 | 327 | *Yan Xia, Xudong Cao, Fang Wen, Gang Hua, and Jian Sun.* 328 | 329 | 1. **Outlier detection with autoencoder ensembles.** ICDM, 2017. [paper](https://research.ibm.com/publications/outlier-detection-with-autoencoder-ensembles) 330 | 331 | *Jinghui Chen, Saket Sathey, Charu Aggarwaly, and Deepak Turaga.* 332 | 333 | 1. **A study of deep convolutional auto-encoders for anomaly detection in videos.** Pattern Recognition Letters, 2018. [paper](https://www.sciencedirect.com/science/article/pii/S0167865517302489) 334 | 335 | *Manassés Ribeiro, AndréEugênio Lazzaretti, and Heitor Silvério Lopes.* 336 | 337 | 1. **Classification-reconstruction learning for open-set recognition.** CVPR, 2019. [paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Yoshihashi_Classification-Reconstruction_Learning_for_Open-Set_Recognition_CVPR_2019_paper.pdf) 338 | 339 | *Ryota Yoshihashi, Shaodi You, Wen Shao, Makoto Iida, Rei Kawakami, and Takeshi Naemura.* 340 | 341 | 1. **Making reconstruction-based method great again for video anomaly detection.** ICDM, 2022. [paper](https://ieeexplore.ieee.org/abstract/document/10027694/) 342 | 343 | *Yizhou Wang, Can Qin, Yue Bai, Yi Xu, Xu Ma, and Yun Fu.* 344 | 345 | 1. **Two-stream decoder feature normality estimating network for industrial snomaly fetection.** ICASSP, 2023. [paper](https://ieeexplore.ieee.org/abstract/document/10027694/) 346 | 347 | *Chaewon Park, Minhyeok Lee, Suhwan Cho, Donghyeong Kim, and Sangyoun Lee.* 348 | 349 | 1. **Synthetic pseudo anomalies for unsupervised video anomaly detection: A simple yet efficient framework based on masked autoencoder.** ICASSP, 2023. [paper](https://arxiv.org/abs/2303.05112) 350 | 351 | *Xiangyu Huang, Caidan Zhao, Chenxing Gao, Lvdong Chen, and Zhiqiang Wu.* 352 | 353 | 1. **Deep autoencoding one-class time series anomaly detection.** ICASSP, 2023. [paper](https://ieeexplore.ieee.org/abstract/document/10095724) 354 | 355 | *Xudong Mou, Rui Wang, Tiejun Wang, Jie Sun, Bo Li, Tianyu Wo, and Xudong Liu.* 356 | 357 | 1. **Reconstruction error-based anomaly detection with few outlying examples.** arXiv, 2023. [paper](https://arxiv.org/abs/2305.10464) 358 | 359 | *Fabrizio Angiulli, Fabio Fassetti, and Luca Ferragina.* 360 | 361 | 1. **LARA: A light and anti-overfitting retraining approach for unsupervised anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.05668) 362 | 363 | *Feiyi Chen, Zhen Qing, Yingying Zhang, Shuiguang Deng, Yi Xiao, Guansong Pang, and Qingsong Wen.* 364 | 365 | 1. **FMM-Head: Enhancing autoencoder-based ECG anomaly detection with prior knowledge.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.05848) 366 | 367 | *Giacomo Verardo, Magnus Boman, Samuel Bruchfeld, Marco Chiesa, Sabine Koch, Gerald Q. Maguire Jr., and Dejan Kostic.* 368 | 369 | 1. **Online multi-view anomaly detection with disentangled product-of-experts modeling.** MM, 2023. [paper](https://arxiv.org/abs/2310.18728) 370 | 371 | *Hao Wang, Zhiqi Cheng, Jingdong Sun, Xin Yang, Xiao Wu, Hongyang Chen, and Yan Yang.* 372 | 373 | 1. **Fast particle-based anomaly detection algorithm with variational autoencoder.** arXiv, 2023. [paper](https://arxiv.org/abs/2311.17162) 374 | 375 | *Ryan Liu, Abhijith Gandrakota, Jennifer Ngadiuba, Maria Spiropulu, and Jean-Roch Vlimant.* 376 | 377 | 1. **Dynamic erasing network based on multi-scale temporal features for weakly supervised video anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2312.01764) 378 | 379 | *Chen Zhang, Guorong Li, Yuankai Qi, Hanhua Ye, Laiyun Qing, Ming-Hsuan Yang, and Qingming Huang.* 380 | 381 | 1. **ACVAE: A novel self-adversarial variational auto-encoder combined with contrast learning for time series anomaly detection.** Neural Networks, 2023. [paper](https://www.sciencedirect.com/science/article/abs/pii/S0893608023007281) 382 | 383 | *Xiaoxia Zhang, Shang Shi, HaiChao Sun, Degang Chen, Guoyin Wang, and Kesheng Wu.* 384 | 385 | 1. **Dual-constraint autoencoder and adaptive weighted similarity spatial attention for unsupervised anomaly detection.** TII, 2023. [paper](https://ieeexplore.ieee.org/abstract/document/10504620) 386 | 387 | *Ruifan Zhang, Hao Wang, Mingyao Feng, Yikun Liu, and Gongping Yang.* 388 | 389 | 1. **Integrating adversarial training strategies into deep autoencoders: A novel aeroengine anomaly detection framework.** EAAI, 2024. [paper](https://www.sciencedirect.com/science/article/abs/pii/S0952197624010145) 390 | 391 | *Lin Lin, Lizheng Zu, Song Fu, Yikun Liu, Sihao Zhang, Shiwei Suo, and Changsheng Tong.* 392 | 393 | 1. **An outlier-resilient autoencoder for representing high-dimensional and incomplete data.** TETCI, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10634222) 394 | 395 | *Di Wu, Yuanpeng Hu, Kechen Liu, Jing Li, Xianmin Wang, Song Deng, Nenggan Zheng, and Xin Luo.* 396 | 397 | ### [GAN](#content) 398 | 1. **Stabilizing adversarially learned one-class novelty detection using pseudo anomalies.** TIP, 2022. [paper](https://ieeexplore.ieee.org/abstract/document/9887825) 399 | 400 | *Muhammad Zaigham Zaheer, Jin-Ha Lee, Arif Mahmood, Marcella Astri, and Seung-Ik Lee.* 401 | 402 | 1. **GAN ensemble for anomaly detection.** AAAI, 2021. [paper](https://ojs.aaai.org/index.php/AAAI/article/view/16530) 403 | 404 | *Han, Xu, Xiaohui Chen, and Liping Liu.* 405 | 406 | 1. **Generative cooperative learning for unsupervised video anomaly detection.** CVPR, 2022. [paper](https://openaccess.thecvf.com/content/CVPR2022/html/Zaheer_Generative_Cooperative_Learning_for_Unsupervised_Video_Anomaly_Detection_CVPR_2022_paper.html) 407 | 408 | *Zaigham Zaheer, Arif Mahmood, M. Haris Khan, Mattia Segu, Fisher Yu, and Seung-Ik Lee.* 409 | 410 | 1. **GAN-based anomaly detection in imbalance problems.** ECCV, 2020. [paper](https://link.springer.com/chapter/10.1007/978-3-030-65414-6_11) 411 | 412 | *Junbong Kim, Kwanghee Jeong, Hyomin Choi, and Kisung Seo.* 413 | 414 | 1. **Old is gold: Redefining the adversarially learned one-class classifier training paradigm.** CVPR, 2020. [paper](https://openaccess.thecvf.com/content_CVPR_2020/html/Zaheer_Old_Is_Gold_Redefining_the_Adversarially_Learned_One-Class_Classifier_Training_CVPR_2020_paper.html) 415 | 416 | *Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid, and Seung-Ik Lee.* 417 | 418 | 1. **Unsupervised anomaly detection with generative adversarial networks to guide marker discovery.** IPMI, 2017. [paper](https://link.springer.com/chapter/10.1007/978-3-319-59050-9_12) 419 | 420 | *Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Ursula Schmidt-Erfurth, and Georg Langs.* 421 | 422 | 1. **Adversarially learned anomaly detection.** ICDM, 2018. [paper](https://ieeexplore.ieee.org/document/8594897) 423 | 424 | *Houssam Zenati, Manon Romain, Chuan-Sheng Foo, Bruno Lecouat, and Vijay Chandrasekhar.* 425 | 426 | 1. **BeatGAN: Anomalous rhythm detection using adversarially generated time series.** IJCAI, 2019. [paper](https://www.ijcai.org/proceedings/2019/616) 427 | 428 | *Bin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye.* 429 | 430 | 1. **Convolutional transformer based dual discriminator generative adversarial networks for video anomaly detection.** MM, 2021. [paper](https://dl.acm.org/doi/abs/10.1145/3474085.3475693) 431 | 432 | *Xinyang Feng, Dongjin Song, Yuncong Chen, Zhengzhang Chen, Jingchao Ni, and Haifeng Chen.* 433 | 434 | 1. **USAD: Unsupervised anomaly detection on multivariate time series.** KDD, 2020. 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[paper](https://openreview.net/forum?id=CanomFZssu) 1136 | 1137 | *Jingyan Chen, Guanghui Zhu, Chunfeng Yuan, and Yihua Huang.* 1138 | 1139 | 1. **Frequency domain-oriented complex graph neural networks for graph classification.** TNNLS, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10409552) 1140 | 1141 | *Youfa Liu and Bo Du.* 1142 | 1143 | 1. **FGAD: Self-boosted knowledge distillation for an effective federated graph anomaly detection framework.** arXiv, 2024. [paper](https://arxiv.org/abs/2402.12761) 1144 | 1145 | *Jinyu Cai, Yunhe Zhang, Zhoumin Lu, Wenzhong Guo, and See-kiong Ng.* 1146 | 1147 | 1. **Generative semi-supervised graph anomaly detection.** arXiv, 2024. [paper](https://arxiv.org/abs/2402.11887) 1148 | 1149 | *Hezhe Qiao, Qingsong Wen, Xiaoli Li, Ee-Peng Lim, and Guansong Pang.* 1150 | 1151 | 1. **Graph structure reshaping against adversarial attacks on graph neural networks.** TKDE, 2024. [paper](https://www.computer.org/csdl/journal/tk/5555/01/10538390/1XcOSbOdJD2) 1152 | 1153 | *Haibo Wang, Chuan Zhou, Xin Chen, Jia Wu, Shirui Pan, Zhao Li, Jilong Wang, and Philip S. Yu.* 1154 | 1155 | 1. **SmoothGNN: Smoothing-based GNN for unsupervised node anomaly detection.** arXiv, 2024. [paper](https://arxiv.org/abs/2405.17525) 1156 | 1157 | *Xiangyu Dong, Xingyi Zhang, Yanni Sun, Lei Chen, Mingxuan Yuan, and Sibo Wang.* 1158 | 1159 | 1. **Learning-based link anomaly detection in continuous-time dynamic graphs.** arXiv, 2024. [paper](https://arxiv.org/abs/2405.18050) 1160 | 1161 | *Tim Poštuvan, Claas Grohnfeldt, Michele Russo, and Giulio Lovisotto.* 1162 | 1163 | 1. **Motif-consistent counterfactuals with adversarial refinement for graph-level anomaly detection.** KDD, 2024. 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Vicky Zhao, Yuan Yao, and Jia Li.* 1178 | 1179 | ### [Time Series](#content) 1180 | 1. **Variational LSTM enhanced anomaly detection for industrial big data.** TII, 2021. [paper](https://ieeexplore.ieee.org/abstract/document/9195000) 1181 | 1182 | *Xiaokang Zhou, Yiyong Hu, Wei Liang, Jianhua Ma, and Qun Jin.* 1183 | 1184 | 1. **Robust anomaly detection for multivariate time series through stochastic recurrent neural network.** KDD, 2019. [paper](https://dl.acm.org/doi/10.1145/3292500.3330672) 1185 | 1186 | *Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, and Dan Pei.* 1187 | 1188 | 1. **DeepLog: Anomaly detection and diagnosis from system logs through deep learning.** CCS, 2017. [paper](https://dl.acm.org/doi/10.1145/3133956.3134015) 1189 | 1190 | *Min Du, Feifei Li, Guineng Zheng, and Vivek Srikumar.* 1191 | 1192 | 1. **Unsupervised anomaly detection with LSTM neural networks.** TNNLS, 2019. [paper](https://ieeexplore.ieee.org/abstract/document/8836638) 1193 | 1194 | *Tolga Ergen and Suleyman Serdar Kozat.* 1195 | 1196 | 1. **LogAnomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs.** IJCAI, 2019. [paper](https://www.ijcai.org/proceedings/2019/658) 1197 | 1198 | *Weibin Meng, Ying Liu, Yichen Zhu, Shenglin Zhang, Dan Pei, Yuqing Liu, Yihao Chen, Ruizhi Zhang, Shimin Tao, Pei Sun, and Rong Zhou.* 1199 | 1200 | 1. **Outlier detection for time series with recurrent autoencoder ensembles.** IJCAI, 2019. [paper](https://dl.acm.org/doi/abs/10.5555/3367243.3367418) 1201 | 1202 | *Tung Kieu, Bin Yang, Chenjuan Guo, and Christian S. Jensen.* 1203 | 1204 | 1. **Learning regularity in skeleton trajectories for anomaly detection in videos.** CVPR, 2019. [paper](https://dl.acm.org/doi/abs/10.5555/3367243.3367418) 1205 | 1206 | *Romero Morais, Vuong Le, Truyen Tran, Budhaditya Saha, Moussa Mansour, and Svetha Venkatesh.* 1207 | 1208 | 1. **LSTM-based encoder-decoder for multi-sensor anomaly detection.** arXiv, 2016. [paper](https://arxiv.org/abs/1607.00148) 1209 | 1210 | *Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff.* 1211 | 1212 | 1. **CrossFuN: Multi-view joint cross fusion network for time series anomaly detection.** TIM, 2023. [paper](https://ieeexplore.ieee.org/abstract/document/10254685) 1213 | 1214 | *Yunfei Bai, Jing Wang, Xueer Zhang, Xiangtai Miao, and Youfang Linf.* 1215 | 1216 | 1. **Unsupervised anomaly detection by densely contrastive learning for time series data.** Neural Networks, 2023. [paper](https://www.sciencedirect.com/science/article/abs/pii/S0893608023005385) 1217 | 1218 | *Wei Zhu, Weijian Li, E. Ray Dorsey, and Jiebo Luo.* 1219 | 1220 | 1. **Algorithmic recourse for anomaly detection in multivariate time series.** arXiv, 2023. [paper](https://arxiv.org/abs/2309.16896) 1221 | 1222 | *Xiao Han, Lu Zhang, Yongkai Wu, and Shuhan Yuan.* 1223 | 1224 | 1. **Unravel anomalies: An end-to-end seasonal-trend decomposition approach for time series anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.00268) 1225 | 1226 | *Zhenwei Zhang, Ruiqi Wang, Ran Ding, and Yuantao Gu.* 1227 | 1228 | 1. **MAG: A novel approach for effective anomaly detection in spacecraft telemetry data.** TII, 2023. [paper](https://ieeexplore.ieee.org/abstract/document/10269707) 1229 | 1230 | *Bing Yu, Yang Yu, Jiakai Xu, Gang Xiang, and Zhiming Yang.* 1231 | 1232 | 1. **Duogat: Dual time-oriented graph attention networks for accurate, efficient and explainable anomaly detection on time-series.** CIKM, 2023. [paper](https://dl.acm.org/doi/abs/10.1145/3583780.3614857) 1233 | 1234 | *Jongsoo Lee, Byeongtae Park, and Dong-Kyu Chae.* 1235 | 1236 | 1. **An enhanced spatio-temporal constraints network for anomaly detection in multivariate time series.** KBS, 2023. [paper](https://www.sciencedirect.com/science/article/abs/pii/S095070512300919X) 1237 | 1238 | *Di Ge, Zheng Dong, Yuhang Cheng, and Yanwen Wu.* 1239 | 1240 | 1. **Asymmetric autoencoder with SVD regularization for multivariate time series anomaly detection.** Neural Networks, 2023. [paper](https://www.sciencedirect.com/science/article/abs/pii/S0893608023006469) 1241 | 1242 | *Yueyue Yao, Jianghong Ma, Shanshan Feng, and Yunming Ye.* 1243 | 1244 | 1. **Unraveling the anomaly in time series anomaly detection: A self-supervised tri-domain solution.** arXiv, 2023. [paper](https://arxiv.org/abs/2311.11235) 1245 | 1246 | *Yuting Sun, Guansong Pang, Guanhua Ye, Tong Chen, Xia Hu, and Hongzhi Yin.* 1247 | 1248 | 1. **A filter-augmented auto-encoder with learnable normalization for robust multivariate time series anomaly detection.** Neural Networks, 2023. [paper](https://www.sciencedirect.com/science/article/abs/pii/S0893608023006706) 1249 | 1250 | *Jiahao Yu, Xin Gao, Baofeng Li, Feng Zhai, Jiansheng Lu, Bing Xue, Shiyuan Fu, and Chun Xiao.* 1251 | 1252 | 1. **MEMTO: Memory-guided Transformer for multivariate time series anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2312.02530) 1253 | 1254 | *Junho Song, Keonwoo Kim, Jeonglyul Oh, and Sungzoon Cho.* 1255 | 1256 | 1. **Entropy causal graphs for multivariate time series anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2312.09478) 1257 | 1258 | *Falih Gozi Febrinanto, Kristen Moore, Chandra Thapa, Mujie Liu, Vidya Saikrishna, Jiangang Ma, and Feng Xia.* 1259 | 1260 | 1. **Label-free multivariate time series anomaly detection.** TKDE, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10380724) 1261 | 1262 | *Qihang Zhou, Shibo He, Haoyu Liu, Jiming Chen, and Wenchao Meng.* 1263 | 1264 | 1. **Quantile-long short term memory: A robust, time series anomaly detection method.** TAI, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10398596) 1265 | 1266 | *Snehanshu Saha, Jyotirmoy Sarkar, Soma Dhavala, Preyank Mota, and Santonu Sarkar.* 1267 | 1268 | 1. **PatchAD: Patch-based mlp-mixer for time series anomaly detection.** arXiv, 2024. [paper](https://arxiv.org/abs/2401.09793) 1269 | 1270 | *Zhijie Zhong, Zhiwen Yu, Yiyuan Yang, Weizheng Wang, and Kaixiang Yang.* 1271 | 1272 | 1. **MELODY: Robust semi-supervised hybrid model for entity-level online anomaly detection with multivariate time series.** arXiv, 2024. [paper](https://arxiv.org/abs/2401.10338v1) 1273 | 1274 | *Jingchao Ni, Gauthier Guinet, Peihong Jiang, Laurent Callot, and Andrey Kan.* 1275 | 1276 | 1. **Understanding time series anomaly state detection through one-class classification.** arXiv, 2024. [paper](https://arxiv.org/abs/2402.02007v1) 1277 | 1278 | *Hanxu Zhou, Yuan Zhang, Guangjie Leng, Ruofan Wang, and Zhi-Qin John Xu.* 1279 | 1280 | 1. **Asymptotic consistent graph structure learning for multivariate time-series anomaly detection.** TIM, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10445316) 1281 | 1282 | *Huaxin Pang, Shikui Wei, Youru Li, Ting Liu, Huaqi Zhang, Ying Qin, and Yao Zhao.* 1283 | 1284 | 1. **Anomaly detection via graph attention networks-augmented mask autoregressive flow for multivariate time series.** IoT, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10453361) 1285 | 1286 | *Hao Liu, Wang Luo, Lixin Han, Peng Gao, Weiyong Yang, and Guangjie Han.* 1287 | 1288 | 1. **From chaos to clarity: Time series anomaly detection in astronomical observations.** arXiv, 2024. [paper](https://arxiv.org/abs/2403.10220) 1289 | 1290 | *Xinli Hao, Yile Chen, Chen Yang, Zhihui Du, Chaohong Ma, Chao Wu, and Xiaofeng Meng.* 1291 | 1292 | 1. **DACAD: Domain adaptation contrastive learning for anomaly detection in multivariate time series.** arXiv, 2024. [paper](https://arxiv.org/abs/2404.11269) 1293 | 1294 | *Zahra Zamanzadeh Darban, Geoffrey I. Webb, and Mahsa Salehi.* 1295 | 1296 | 1. **Variate associated domain adaptation for unsupervised multivariate time series anomaly detection.** TKDD, 2024. [paper](https://dl.acm.org/doi/10.1145/3663573) 1297 | 1298 | *Yifan He, Yatao Bian, Xi Ding, Bingzhe Wu, Jihong Guan, Ji Zhang, and Shuigeng Zhou.* 1299 | 1300 | 1. **Position: Quo vadis, unsupervised time series anomaly detection?** ICML, 2024. [paper](https://openreview.net/forum?id=W7Vqx1Jvc2) 1301 | 1302 | *M. Saquib Sarfraz, Meiyen Chen, Lukas Layer, Kunyu Peng, and Marios Koulakis.* 1303 | 1304 | 1. **SiET: Spatial information enhanced transformer for multivariate time series anomaly detection.** KBS, 2024. [paper](https://www.sciencedirect.com/science/article/abs/pii/S0950705124005628) 1305 | 1306 | *Weixuan Xiong, Peng Wang, Xiaochen Sun, and Jun Wang.* 1307 | 1308 | 1. **Disentangled anomaly detection for multivariate time seriesn.** WWW, 2024. [paper](https://dl.acm.org/doi/abs/10.1145/3589335.3651492) 1309 | 1310 | *Xin Jie, Xixi Zhou, Chanfei Su, Zijun Zhou, Yuqing Yuan, Jiajun Bu, and Haishuai Wang.* 1311 | 1312 | 1. **PATE: Proximity-aware time series anomaly evaluation.** KDD, 2024. [paper](https://arxiv.org/abs/2405.12096) 1313 | 1314 | *Ramin Ghorbani, Marcel J.T. Reinders, and David M.J. Tax.* 1315 | 1316 | 1. **Large language models can be zero-shot anomaly detectors for time series?** KDD, 2024. [paper](https://arxiv.org/abs/2405.14755) 1317 | 1318 | *Sarah Alnegheimish, Linh Nguyen, Laure Berti-Equille, and Kalyan Veeramachaneni.* 1319 | 1320 | 1. **LARA: A light and anti-overfitting retraining approach for unsupervised time series anomaly detection.** WWW, 2024. [paper](https://dl.acm.org/doi/abs/10.1145/3589334.3645472) 1321 | 1322 | *Feiyi Chen, Zhen Qin, Mengchu Zhou, Yingying Zhang, Shuiguang Deng, Lunting Fan, Guansong Pang, and Qingsong Wen.* 1323 | 1324 | 1. **Variate associated domain adaptation for unsupervised multivariate time series anomaly detection.** TKDD, 2024. [paper](https://dl.acm.org/doi/abs/10.1145/3663573) 1325 | 1326 | *Yifan He, Yatao Bian, Xi Ding, Bingzhe Wu, Jihong Guan, Ji Zhang, and Shuigeng Zhou.* 1327 | 1328 | 1. **Uni-directional graph structure learning-based multivariate time series anomaly detection with dynamic prior knowledge.** International Journal of Machine Learning and Cybernetics, 2024. [paper](https://link.springer.com/article/10.1007/s13042-024-02212-5) 1329 | 1330 | *Shiming He, Genxin Li, Jin Wang, Kun Xie, and Pradip Kumar Sharma.* 1331 | 1332 | 1. **Towards a general time series anomaly detector with adaptive bottlenecks and dual adversarial decoders.** arXiv, 2024. [paper](https://arxiv.org/abs/2405.15273) 1333 | 1334 | *Qichao Shentu, Beibu Li, Kai Zhao, Yang shu, Zhongwen Rao, Lujia Pan, Bin Yang, and Chenjuan Guo.* 1335 | 1336 | 1. **USD: Unsupervised soft contrastive learning for fault detection in multivariate time series.** arXiv, 2024. [paper](https://arxiv.org/abs/2405.16258) 1337 | 1338 | *Hong Liu, Xiuxiu Qiu, Yiming Shi, and Zelin Zang.* 1339 | 1340 | 1. **FITS: Modeling time series with 10k parameters.** ICLR, 2024. [paper](https://openreview.net/forum?id=bWcnvZ3qMb) 1341 | 1342 | *Zhijian Xu, Ailing Zeng, and Qiang Xu.* 1343 | 1344 | 1. **LARA: A light and anti-overfitting retraining approach for unsupervised time series anomaly detection.** WWW, 2024. [paper](https://dl.acm.org/doi/abs/10.1145/3589334.3645472) 1345 | 1346 | *Feiyi Chen, Zhen Qin, Mengchu Zhou, Yingying Zhang, Shuiguang Deng, Lunting Fan, Guansong Pang, and Qingsong Wen.* 1347 | 1348 | 1. **Unraveling anomalies in time: Unsupervised discovery and isolation of anomalous behavior in bio-regenerative life support system telemetry.** ECML PKDD, 2024. [paper](https://arxiv.org/abs/2406.09825) 1349 | 1350 | *Ferdinand Rewicki, Jakob Gawlikowski, Julia Niebling, and Joachim Denzler.* 1351 | 1352 | 1. **T-Rep: Representation learning for time series using time-embeddings.** ICLR, 2024. [paper](https://openreview.net/forum?id=3y2TfP966N) 1353 | 1354 | *Archibald Felix Fraikin, Adrien Bennetot, and Stephanie Allassonniere.* 1355 | 1356 | 1. **SensitiveHUE: Multivariate time series anomaly detection by enhancing the sensitivity to normal patterns.** KDD, 2024. [paper](https://dl.acm.org/doi/abs/10.1145/3637528.3671919) 1357 | 1358 | *Yuye Feng, Wei Zhang, Yao Fu, Weihao Jiang, Jiang Zhu, and Wenqi Ren.* 1359 | 1360 | 1. **CutAddPaste: Time series anomaly detection by exploiting abnormal knowledge.** KDD, 2024. [paper](https://dl.acm.org/doi/abs/10.1145/3637528.3671739) 1361 | 1362 | *Rui Wang, Xudong Mou, Renyu Yang, Kai Gao, Pin Liu, Chongwei Liu, Tianyu Wo, and Xudong Liu.* 1363 | 1364 | 1. **ALAD: A new unsupervised time series anomaly detection paradigm based on activation learning.** TBDATA, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10663916) 1365 | 1366 | *Fengqian Ding, Bo Li, Xianye Ben, Jia Zhao, and Hongchao Zhou.* 1367 | 1368 | 1. **Fusion graph structure learning-based multivariate time series anomaly detection with structured prior knowledge.** TIFS, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10679558) 1369 | 1370 | *Shiming He, Genxin Li, Kun Xie, and Pradip Kumar Sharma.* 1371 | 1372 | 1. **Rethinking robust multivariate time series anomaly detection: A hierarchical spatio-temporal variational perspective.** TKDE, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10689345) 1373 | 1374 | *Xiao Zhang, Shuqing Xu, Huashan Chen, Zekai Chen, Fuzhen Zhuang, Hui Xiong, and Dongxiao Yu.* 1375 | 1376 | 1. **Enhancing multivariate time series anomaly detection with an inference stacked recurrent-autoencoder in strong mechanistic contexts.** TII, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10752835) 1377 | 1378 | *Tianming Xie, Zhiwei Gao, Qifa Xu, Cuixia Jiang, and Aihua Zhang.* 1379 | 1380 | ### [Tabular](#content) 1381 | 1. **Beyond individual input for deep anomaly detection on tabular data.** ICML, 2024. [paper](https://openreview.net/forum?id=chDpBp2P6b) 1382 | 1383 | *Hugo Thimonier, Fabrice Popineau, Arpad Rimmel, and Bich-Liên Doan.* 1384 | 1385 | 1. **Fascinating supervisory signals and where to find them: Deep anomaly detection with scale learning.** ICML, 2023. [paper](https://arxiv.org/abs/2305.16114) 1386 | 1387 | *Hongzuo Xu, Yijie Wang, Juhui Wei, Songlei Jian, Yizhou Li, and Ning Liu.* 1388 | 1389 | 1. **TabADM: Unsupervised tabular anomaly detection with diffusion models.** arXiv, 2023. [paper](https://arxiv.org/abs/2307.12336) 1390 | 1391 | *Guy Zamberg, Moshe Salhov, Ofir Lindenbaum, and Amir Averbuch.* 1392 | 1393 | 1. **ATDAD: One-class adversarial learning for tabular data anomaly detection.** Computers & Security, 2023. [paper](https://www.sciencedirect.com/science/article/abs/pii/S0167404823003590) 1394 | 1395 | *Xiaohui Yang and Xiang Li.* 1396 | 1397 | 1. **Understanding the limitations of self-supervised learning for tabular anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2309.08374) 1398 | 1399 | *Kimberly T. Mai, Toby Davies, and Lewis D. Griffin.* 1400 | 1401 | 1. **Unmasking the chameleons: A benchmark for out-of-distribution detection in medical tabular data.** arXiv, 2023. [paper](https://arxiv.org/abs/2309.16220) 1402 | 1403 | *Mohammad Azizmalayeri, Ameen Abu-Hanna, and Giovanni Ciná.* 1404 | 1405 | 1. **TDeLTA: A light-weight and robust table detection method based on learning text arrangement.** AAAI, 2024. [paper](https://arxiv.org/abs/2309.16220) 1406 | 1407 | *Yang Fan, Xiangping Wu, Qingcai Chen, Heng Li, Yan Huang, Zhixiang Cai, and Qitian Wu.* 1408 | 1409 | 1. **How to overcome curse-of-dimensionality for out-of-distribution detection?** AAAI, 2024. [paper](https://arxiv.org/abs/2312.11043) 1410 | 1411 | *Soumya Suvra Ghosal, Yiyou Sun, and Yixuan Li.* 1412 | 1413 | 1. **MCM: Masked cell modeling for anomaly detection in tabular data.** ICLR, 2024. [paper](https://openreview.net/forum?id=lNZJyEDxy4) 1414 | 1415 | *Jiaxin Yin, Yuanyuan Qiao, Zitang Zhou, Xiangchao Wang, and Jie Yang.* 1416 | 1417 | ### [Large Model](#content) 1418 | 1. **WinCLIP: Zero-/few-shot anomaly classification and segmentation.** CVPR, 2023. [paper](https://arxiv.org/abs/2303.05047) 1419 | 1420 | *Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, and Onkar Dabeer.* 1421 | 1422 | 1. **Semantic anomaly detection with large language models.** arXiv, 2023. [paper](https://arxiv.org/abs/2305.11307) 1423 | 1424 | *Amine Elhafsi, Rohan Sinha, Christopher Agia, Edward Schmerling, Issa Nesnas, and Marco Pavone.* 1425 | 1426 | 1. **AnomalyGPT: Detecting industrial anomalies using large vision-language models.** arXiv, 2023. [paper](https://arxiv.org/abs/2308.15366) 1427 | 1428 | *Zhaopeng Gu, Bingke Zhu, Guibo Zhu, Yingying Chen, Ming Tang, and Jinqiao Wang.* 1429 | 1430 | 1. **AnoVL: Adapting vision-language models for unified zero-shot anomaly localization.** arXiv, 2023. [paper](https://arxiv.org/abs/2308.15939) 1431 | 1432 | *Hanqiu Deng, Zhaoxiang Zhang, Jinan Bao, and Xingyu Li.* 1433 | 1434 | 1. **LogGPT: Exploring ChatGPT for log-based anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2309.01189) 1435 | 1436 | *Jiaxing Qi, Shaohan Huang, Zhongzhi Luan, Carol Fung, Hailong Yang, and Depei Qian.* 1437 | 1438 | 1. **CLIPN for zero-shot OOD detection: Teaching CLIP to say no.** ICCV, 2023. [paper](https://arxiv.org/abs/2308.12213) 1439 | 1440 | *Hualiang Wang, Yi Li, Huifeng Yao, and Xiaomeng Li.* 1441 | 1442 | 1. **LogGPT: Log anomaly detection via GPT.** arXiv, 2023. [paper](https://arxiv.org/abs/2309.14482) 1443 | 1444 | *Xiao Han, Shuhan Yuan, and Mohamed Trabelsi.* 1445 | 1446 | 1. **Semantic scene difference detection in daily life patroling by mobile robots using pre-trained large-scale vision-language model.** IROS, 2023. [paper](https://arxiv.org/abs/2309.16552) 1447 | 1448 | *Yoshiki Obinata, Kento Kawaharazuka, Naoaki Kanazawa, Naoya Yamaguchi, Naoto Tsukamoto, Iori Yanokura, Shingo Kitagawa, Koki Shinjo, Kei Okada, and Masayuki Inaba.* 1449 | 1450 | 1. **HuntGPT: Integrating machine learning-based anomaly detection and explainable AI with large language models (LLMs).** arXiv, 2023. [paper](https://arxiv.org/abs/2309.16021) 1451 | 1452 | *Tarek Ali and Panos Kostakos.* 1453 | 1454 | 1. **Graph neural architecture search with GPT-4.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.01436) 1455 | 1456 | *Haishuai Wang, Yang Gao, Xin Zheng, Peng Zhang, Hongyang Chen, and Jiajun Bu.* 1457 | 1458 | 1. **Exploring large language models for multi-modal out-of-distribution detection.** EMNLP, 2023. [paper](https://arxiv.org/abs/2310.08027) 1459 | 1460 | *Yi Dai, Hao Lang, Kaisheng Zeng, Fei Huang, and Yongbin Li.* 1461 | 1462 | 1. **Detecting pretraining data from large language models.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.16789) 1463 | 1464 | *Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer.* 1465 | 1466 | 1. **AnomalyCLIP: Object-agnostic prompt learning for zero-shot anomaly detection.** ICLR, 2024. [paper](https://openreview.net/forum?id=buC4E91xZE) 1467 | 1468 | *Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, and Jiming Chen.* 1469 | 1470 | 1. **CLIP-AD: A language-guided staged dual-path model for zero-shot anomaly detection.** arXiv, 2023. 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[paper](https://arxiv.org/abs/2402.04857) 2138 | 2139 | *Liyun Zhu, Arjun Raj, and Lei Wang.* 2140 | 2141 | 1. **TimeSeriesBench: An industrial-grade benchmark for time series anomaly detection models.** arXiv, 2024. [paper](https://arxiv.org/abs/2402.10802) 2142 | 2143 | *Haotian Si, Changhua Pei, Hang Cui, Jingwen Yang, Yongqian Sun, Shenglin Zhang, Jingjing Li, Haiming Zhang, Jing Han, Dan Pei, Jianhui Li, and Gaogang Xie.* 2144 | 2145 | 1. **Towards fair graph anomaly detection: Problem, new datasets, and evaluation.** arXiv, 2024. [paper](https://arxiv.org/abs/2402.15988) 2146 | 2147 | *Neng Kai Nigel Neo, Yeon-Chang Lee, Yiqiao Jin, Sang-Wook Kim, and Srijan Kumar.* 2148 | 2149 | 1. **Real-IAD: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection.** CVPR, 2024. [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Real-IAD_A_Real-World_Multi-View_Dataset_for_Benchmarking_Versatile_Industrial_Anomaly_CVPR_2024_paper.html) 2150 | 2151 | *Chengjie Wang, Wenbing Zhu, Binbin Gao, Zhenye Gan, Jianning Zhang, Zhihao Gu, Shuguang Qian, Mingang Chen, and Lizhuang Ma.* 2152 | 2153 | 1. **MTMMC: A large-scale real-world multi-modal camera tracking benchmark.** CVPR, 2024. [paper](https://arxiv.org/abs/2403.20225) 2154 | 2155 | *Sanghyun Woo, Kwanyong Park, Inkyu Shin, Myungchul Kim, and In So Kweon.* 2156 | 2157 | 1. **IPAD: Industrial process anomaly detection dataset.** arXiv, 2024. [paper](https://arxiv.org/abs/2402.04857) 2158 | 2159 | *Jinfan Liu, Yichao Yan, Junjie Li, Weiming Zhao, Pengzhi Chu, Xingdong Sheng, Yunhui Liu, and Xiaokang Yang.* 2160 | 2161 | 1. **Supervised anomaly detection for complex industrial images.** CVPR, 2024. [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Baitieva_Supervised_Anomaly_Detection_for_Complex_Industrial_Images_CVPR_2024_paper.html) 2162 | 2163 | *Aimira Baitieva, David Hurych, Victor Besnier, and Olivier Bernard.* 2164 | 2165 | 1. **ADer: A comprehensive benchmark for multi-class visual anomaly detection.** arXiv, 2024. [paper](https://arxiv.org/abs/2406.03262) 2166 | 2167 | *Jiangning Zhang, Haoyang He, Zhenye Gan, Qingdong He, Yuxuan Cai, Zhucun Xue, Yabiao Wang, Chengjie Wang, Lei Xie, and Yong Liu.* 2168 | 2169 | 1. **SeMAnD: Self-supervised anomaly detection in multimodal geospatial datasets.** ACM SIGSPATIAL, 2023. [paper](https://arxiv.org/abs/2309.15245) 2170 | 2171 | *Daria Reshetova, Swetava Ganguli, C. V. Krishnakumar Iyer, and Vipul Pandey.* 2172 | 2173 | 1. **FT-AED: Benchmark dataset for early freeway traffic anomalous event Detection.** arXiv, 2024. [paper](https://arxiv.org/abs/2406.15283) 2174 | 2175 | *Austin Coursey, Junyi Ji, Marcos Quinones-Grueiro, William Barbour, Yuhang Zhang, Tyler Derr, and Gautam Biswas.* 2176 | 2177 | 1. **UMAD: University of Macau anomaly detection benchmark dataset.** IROS, 2024. [paper](https://arxiv.org/abs/2408.12527) 2178 | 2179 | *Dong Li, Lineng Chen, Chengzhong Xu, and Hui Kong.* 2180 | 2181 | 1. **PHEVA: A privacy-preserving human-centric video anomaly detection dataset.** arXiv, 2024. [paper](https://arxiv.org/abs/2408.14329) 2182 | 2183 | *Ghazal Alinezhad Noghre, Shanle Yao, Armin Danesh Pazho, Babak Rahimi Ardabili, Vinit Katariya, and Hamed Tabkhi.* 2184 | 2185 | 1. **3CAD: A large-scale real-world 3C product dataset for unsupervised anomaly.** AAAI, 2025. [paper](https://arxiv.org/abs/2408.14329) 2186 | 2187 | *Enquan Yang, Peng Xing, Hanyang Sun, Wenbo Guo, Yuanwei Ma, Zechao Li, and Dan Zeng.* 2188 | 2189 | 1. **MANTA: A large-scale multi-view and visual-text anomaly aetection dataset for tiny objects.** CVPR, 2025. [paper](https://arxiv.org/abs/2412.04867) 2190 | 2191 | *Lei Fan, Dongdong Fan, Zhiguang Hu, Yiwen Ding, Donglin Di, Kai Yi, Maurice Pagnucco, and Yang Song.* 2192 | 2193 | ### [Benchmark](#content) 2194 | 1. **ADBench: Anomaly detection benchmark.** NIPS, 2022. [paper](https://openreview.net/forum?id=foA_SFQ9zo0) 2195 | 2196 | *Songqiao Han, Xiyang Hu, Hailiang Huang, Minqi Jiang, and Yue Zhao.* 2197 | 2198 | 1. **TSB-UAD: An end-to-end benchmark suite for univariate time-series anomaly detection.** VLDB, 2022. [paper](https://dl.acm.org/doi/abs/10.14778/3529337.3529354) 2199 | 2200 | *John Paparrizos, Yuhao Kang, Paul Boniol, Ruey S. Tsay, Themis Palpanas, and Michael J. Franklin.* 2201 | 2202 | 1. **PyOD: A python toolbox for scalable outlier detection.** JMLR, 2019. [paper](https://www.jmlr.org/papers/v20/19-011.html) 2203 | 2204 | *Yue Zhao, Zain Nasrullah, and Zheng Li.* 2205 | 2206 | 1. **OpenOOD: Benchmarking generalized out-of-distribution detection.** NIPS, 2022. [paper](https://arxiv.org/abs/2210.07242) 2207 | 2208 | *Jingkang Yang, Pengyun Wang, Dejian Zou, Zitang Zhou, Kunyuan Ding, Wenxuan Peng, Haoqi Wang, Guangyao Chen, Bo Li, Yiyou Sun, Xuefeng Du, Kaiyang Zhou, Wayne Zhang, Dan Hendrycks, Yixuan Li, and Ziwei Liu.* 2209 | 2210 | 1. **Towards a rigorous evaluation of rime-series anomaly detection.** AAAI, 2022. [paper](https://ojs.aaai.org/index.php/AAAI/article/view/20680) 2211 | 2212 | *Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, and Sungroh Yoon.* 2213 | 2214 | 1. **Volume under the surface: A new accuracy evaluation measure for time-series anomaly detection.** VLDB, 2022. [paper](https://dl.acm.org/doi/abs/10.14778/3551793.3551830) 2215 | 2216 | *John Paparrizos, Paul Boniol, Themis Palpanas, Ruey S. Tsa, Aaron Elmore, and Michael J. Franklin.* 2217 | 2218 | 1. **AnomalyKiTS: Anomaly detection toolkit for time series.** AAAI, 2020. [paper](https://ojs.aaai.org/index.php/AAAI/article/view/21730) 2219 | 2220 | *Dhaval Patel, Giridhar Ganapavarapu, Srideepika Jayaraman, Shuxin Lin, Anuradha Bhamidipaty, and Jayant Kalagnanam.* 2221 | 2222 | 1. **TODS: An automated time series outlier detection system.** AAAI, 2021. [paper](https://ojs.aaai.org/index.php/AAAI/article/view/18012) 2223 | 2224 | *Kwei-Herng Lai, Daochen Zha, Guanchu Wang, Junjie Xu, Yue Zhao, Devesh Kumar, Yile Chen, Purav Zumkhawaka, Minyang Wan, Diego Martinez, and Xia Hu.* 2225 | 2226 | 1. **BOND: Benchmarking unsupervised outlier node detection on static attributed graphs.** NIPS, 2022. [paper](https://openreview.net/forum?id=YXvGXEmtZ5N) 2227 | 2228 | *Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, and Philip S. Yu.* 2229 | 2230 | 1. **Ubnormal: New benchmark for supervised open-set video anomaly detection.** CVPR, 2022. [paper](https://openaccess.thecvf.com/content/CVPR2022/html/Acsintoae_UBnormal_New_Benchmark_for_Supervised_Open-Set_Video_Anomaly_Detection_CVPR_2022_paper.html) 2231 | 2232 | *Andra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor Ionescu, Fahad Shahbaz Khan, and Mubarak Shah.* 2233 | 2234 | 1. **A new comprehensive benchmark for semi-supervised video anomaly detection and anticipation.** CVPR, 2023. [paper](https://arxiv.org/abs/2305.13611) 2235 | 2236 | *Congqi Cao, Yue Lu, Peng Wang, and Yanning Zhang.* 2237 | 2238 | 1. **A framework for benchmarking class-out-of-distribution detection and its application to ImageNet.** ICLR, 2023. [paper](https://openreview.net/forum?id=Iuubb9W6Jtk) 2239 | 2240 | *Ido Galil, Mohammed Dabbah, and Ran El-Yaniv.* 2241 | 2242 | 1. **BMAD: Benchmarks for medical anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2306.11876) 2243 | 2244 | *Jinan Bao, Hanshi Sun, Hanqiu Deng, Yinsheng He, Zhaoxiang Zhang, and Xingyu Li.* 2245 | 2246 | 1. **GADBench: Revisiting and benchmarking supervised Graph anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2306.12251) 2247 | 2248 | *Jianheng Tang, Fengrui Hua, Ziqi Gao, Peilin Zhao, and Jia Li.* 2249 | 2250 | 1. **A framework for benchmarking class-out-of-distribution detection and its application to ImageNet.** ICLR, 2023. [paper](https://arxiv.org/abs/2302.11893) 2251 | 2252 | *Ido Galil, Mohammed Dabbah, and Ran El-Yaniv.* 2253 | 2254 | 1. **A large-scale benchmark for log parsing.** arXiv, 2023. [paper](https://arxiv.org/abs/2308.10828) 2255 | 2256 | *Zhihan Jiang, Jinyang Liu, Junjie Huang, Yichen Li, Yintong Huo, Jiazhen Gu, Zhuangbin Chen, Jieming Zhu, and Michael R. Lyu.* 2257 | 2258 | 1. **Making the end-user a priority in benchmarking: OrionBench for unsupervised time series anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.17748) 2259 | 2260 | *Sarah Alnegheimish, Laure Berti-Equille, and Kalyan Veeramachaneni.* 2261 | 2262 | 1. **Towards scalable 3D anomaly detection and localization: A benchmark via 3D anomaly synthesis and a self-supervised learning network.** arXiv, 2023. [paper](https://arxiv.org/abs/2311.14897) 2263 | 2264 | *Wenqiao Li and Xiaohao Xu.* 2265 | 2266 | 1. **AUPIMO: Redefining visual anomaly detection benchmarks with high speed and low tolerance.** arXiv, 2024. [paper](https://arxiv.org/abs/2401.01984) 2267 | 2268 | *Joao P. C. Bertoldo, Dick Ameln, Ashwin Vaidya, and Samet Akçay.* 2269 | 2270 | 1. **MTAD: Tools and benchmarks for multivariate time series anomaly detection.** arXiv, 2024. [paper](https://arxiv.org/abs/2401.06175) 2271 | 2272 | *Jinyang Liu, Wenwei Gu, Zhuangbin Chen, Yichen Li, Yuxin Su, and Michael R. Lyu.* 2273 | 2274 | 1. **Vane-Bench: Video anomaly evaluation benchmark for conversational LMMs.** arXiv, 2024. [paper](https://arxiv.org/abs/2406.10326) 2275 | 2276 | *Rohit Bharadwaj, Hanan Gani, Muzammal Naseer, Fahad Shahbaz Khan, and Salman Khan.* 2277 | 2278 | 1. **Towards scalable 3D anomaly detection and localization: A benchmark via 3D anomaly synthesis and a self-supervised learning network.** CVPR, 2024. [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Li_Towards_Scalable_3D_Anomaly_Detection_and_Localization_A_Benchmark_via_CVPR_2024_paper.html) 2279 | 2280 | *Wenqiao Li, Xiaohao Xu, Yao Gu, Bozhong Zheng, Shenghua Gao, and Yingna Wu.* 2281 | 2282 | 1. **Uncovering what why and how: A comprehensive benchmark for causation understanding of video anomaly.** CVPR, 2024. [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Du_Uncovering_What_Why_and_How_A_Comprehensive_Benchmark_for_Causation_CVPR_2024_paper.html) 2283 | 2284 | *Hang Du, Sicheng Zhang, Binzhu Xie, Guoshun Nan, Jiayang Zhang, Junrui Xu, Hangyu Liu, Sicong Leng, Jiangming Liu, Hehe Fan, Dajiu Huang, Jing Feng, Linli Chen, Can Zhang, Xuhuan Li, Hao Zhang, Jianhang Chen, Qimei Cui, and Xiaofeng Tao.* 2285 | 2286 | 1. **OpenCIL: Benchmarking out-of-distribution detection in class-incremental learning.** arXiv, 2024. [paper](https://arxiv.org/abs/2407.06045) 2287 | 2288 | *Wenjun Miao, Guansong Pang, Trong-Tung Nguyen, Ruohang Fang, Jin Zheng, and Xiao Bai.* 2289 | 2290 | ### [Investigation](#content) 2291 | 1. **Are we certain it’s anomalous?** arXiv, 2022. [paper](https://arxiv.org/abs/2211.09224) 2292 | 2293 | *Alessandro Flaborea, Bardh Prenkaj, Bharti Munjal, Marco Aurelio Sterpa, Dario Aragona, Luca Podo, and Fabio Galasso.* 2294 | 2295 | 1. **Understanding anomaly detection with deep invertible networks through hierarchies of distributions and features.** NIPS, 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/f106b7f99d2cb30c3db1c3cc0fde9ccb-Abstract.html) 2296 | 2297 | *Robin Schirrmeister, Yuxuan Zhou, Tonio Ball, and Dan Zhang.* 2298 | 2299 | 1. **Further analysis of outlier detection with deep generative models.** NIPS, 2018. [paper](http://proceedings.mlr.press/v137/wang20a.html) 2300 | 2301 | *Ziyu Wang, Bin Dai, David Wipf, and Jun Zhu.* 2302 | 2303 | 1. **Learning temporal regularity in video sequences.** CVPR, 2016. [paper](https://openaccess.thecvf.com/content_cvpr_2016/html/Hasan_Learning_Temporal_Regularity_CVPR_2016_paper.html) 2304 | 2305 | *Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K. Roy-Chowdhury, and Larry S. Davis.* 2306 | 2307 | 1. **Local evaluation of time series anomaly detection algorithms.** KDD, 2022. [paper](https://dl.acm.org/doi/abs/10.1145/3534678.3539339) 2308 | 2309 | *Alexis Huet, Jose Manuel Navarro, and Dario Rossi.* 2310 | 2311 | 1. **Adaptive model pooling for online deep anomaly detection from a complex evolving data stream.** KDD, 2022. [paper](https://dl.acm.org/doi/abs/10.1145/3534678.3539348) 2312 | 2313 | *Susik Yoon, Youngjun Lee, Jae-Gil Lee, and Byung Suk Lee.* 2314 | 2315 | 1. **Anomaly detection in time series: A comprehensive evaluation.** VLDB, 2022. [paper](https://dl.acm.org/doi/abs/10.14778/3538598.3538602) 2316 | 2317 | *Sebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock.* 2318 | 2319 | 1. **Anomaly detection requires better representations.** arXiv, 2022. [paper](https://arxiv.org/abs/2210.10773) 2320 | 2321 | *Tal Reiss, Niv Cohen, Eliahu Horwitz, Ron Abutbul, and Yedid Hoshen.* 2322 | 2323 | 1. **Is it worth it? An experimental comparison of six deep and classical machine learning methods for unsupervised anomaly detection in time series.** arXiv, 2022. [paper](https://arxiv.org/abs/2212.11080) 2324 | 2325 | *Ferdinand Rewicki, Joachim Denzler, and Julia Niebling.* 2326 | 2327 | 1. **FAPM: Fast adaptive patch memory for real-time industrial anomaly detection.** arXiv, 2022. [paper](https://arxiv.org/abs/2210.07548) 2328 | 2329 | *Shinji Yamada, Satoshi Kamiya, and Kazuhiro Hotta.* 2330 | 2331 | 1. **Detecting data errors: Where are we and what needs to be done?** VLDB, 2016. 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[paper](https://dl.acm.org/doi/10.1145/2882903.2912574) 2348 | 2349 | *Radu Tudor Ionescu, Sorina Smeureanu, Bogdan Alexe, and Marius Popescu.* 2350 | 2351 | 1. **Temporal cycle-consistency learning.** CVPR, 2019. [paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Dwibedi_Temporal_Cycle-Consistency_Learning_CVPR_2019_paper.pdf) 2352 | 2353 | *Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman.* 2354 | 2355 | 1. **Look at adjacent frames: Video anomaly detection without offline training.** ECCV, 2022. [paper](https://arxiv.org/abs/2207.13798) 2356 | 2357 | *Yuqi Ouyang, Guodong Shen, and Victor Sanchez.* 2358 | 2359 | 1. **How to allocate your label budget? Choosing between active learning and learning to reject in anomaly detection.** AAAI, 2023. [paper](https://arxiv.org/abs/2301.02909) 2360 | 2361 | *Lorenzo Perini, Daniele Giannuzzi, and Jesse Davis.* 2362 | 2363 | 1. **Deep anomaly detection under labeling budget constraints.** arXiv, 2023. [paper](https://arxiv.org/abs/2302.07832) 2364 | 2365 | *Aodong Li, Chen Qiu, Padhraic Smyth, Marius Kloft, Stephan Mandt, and Maja Rudolph.* 2366 | 2367 | 1. **Diversity-measurable anomaly detection.** CVPR, 2023. [paper](https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Diversity-Measurable_Anomaly_Detection_CVPR_2023_paper.html) 2368 | 2369 | *Wenrui Liu, Hong Chang, Bingpeng Ma, Shiguang Shan, and Xilin Chen.* 2370 | 2371 | 1. **Transferring the contamination factor between anomaly detection domains by shape similarity.** AAAI, 2022. [paper](https://ojs.aaai.org/index.php/AAAI/article/view/20331) 2372 | 2373 | *Lorenzo Perini, Vincent Vercruyssen, and Jesse Davis.* 2374 | 2375 | 1. **Are transformers effective for time series forecasting?** AAAI, 2023. 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[paper](https://arxiv.org/abs/2307.10653) 2392 | 2393 | *Manqing Dong, Zhanxiang Zhao, Yitong Geng, Wentao Li, Wei Wang, and Huai Jiang.* 2394 | 2395 | 1. **Beyond sharing: Conflict-aware multivariate time series anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2308.08915) 2396 | 2397 | *Haotian Si, Changhua Pei, Zhihan Li, Yadong Zhao, Jingjing Li, Haiming Zhang, Zulong Diao, Jianhui Li, Gaogang Xie, and Dan Pei.* 2398 | 2399 | 1. **Neural network training strategy to enhance anomaly detection performance: A perspective on reconstruction loss amplification.** arXiv, 2023. [paper](https://arxiv.org/abs/2308.14595) 2400 | 2401 | *YeongHyeon Park, Sungho Kang, Myung Jin Kim, Hyeonho Jeong, Hyunkyu Park, Hyeong Seok Kim, and Juneho Yi.* 2402 | 2403 | 1. **Self-supervision for tackling unsupervised anomaly detection: Pitfalls and opportunities.** arXiv, 2023. [paper](https://arxiv.org/abs/2308.14380) 2404 | 2405 | *Leman Akoglu and Jaemin Yoo.* 2406 | 2407 | 1. **Tackling diverse minorities in imbalanced classification.** CIKM, 2023. [paper](https://arxiv.org/abs/2308.14838) 2408 | 2409 | *Kwei-Herng Lai, Daochen Zha, Huiyuan Chen, Mangesh Bendre, Yuzhong Chen, Mahashweta Das, Hao Yang, and Xia Hu.* 2410 | 2411 | 1. **IOMatch: Simplifying open-set semi-supervised learning with joint inliers and outliers utilization.** ICCV, 2023. [paper](https://arxiv.org/abs/2308.13168) 2412 | 2413 | *Zekun Li, Lei Qi, Yinghuan Shi, and Yang Gao.* 2414 | 2415 | 1. **Environment-biased feature ranking for novelty detection robustness.** ICCV, 2024. [paper](https://arxiv.org/abs/2309.12301) 2416 | 2417 | *Stefan Smeu, Elena Burceanu, Emanuela Haller, and Andrei Liviu Nicolicioiu.* 2418 | 2419 | 1. **Going beyond familiar features for deep anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.00797) 2420 | 2421 | *Sarath Sivaprasad and Mario Fritz.* 2422 | 2423 | 1. **Template-guided hierarchical feature restoration for anomaly detection.** ICCV, 2023. [paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Guo_Template-guided_Hierarchical_Feature_Restoration_for_Anomaly_Detection_ICCV_2023_paper.pdf) 2424 | 2425 | *Hewei Guo, Liping Ren, Jingjing Fu, Yuwang Wang, Zhizheng Zhang, Cuiling Lan, Haoqian Wang, and Xinwen Hou.* 2426 | 2427 | 1. **Anomaly detection in the presence of irrelevant features.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.13057) 2428 | 2429 | *Marat Freytsis, Maxim Perelstein, and Yik Chuen San.* 2430 | 2431 | 1. **BatchNorm-based weakly supervised video anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2311.15367) 2432 | 2433 | *Yixuan Zhou, Yi Qu, Xing Xu, Fumin Shen, Jingkuan Song, and Hengtao Shen.* 2434 | 2435 | 1. **ADT: Agent-based dynamic thresholding for anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2312.01488) 2436 | 2437 | *Xue Yang, Enda Howley, and Micheal Schukat.* 2438 | 2439 | 1. **PNI : Industrial anomaly detection using position and neighborhood information.** ICCV, 2023. [paper](https://openaccess.thecvf.com/content/ICCV2023/html/Bae_PNI__Industrial_Anomaly_Detection_using_Position_and_Neighborhood_Information_ICCV_2023_paper.html) 2440 | 2441 | *Jaehyeok Bae, Jae-Han Lee, and Seyun Kim.* 2442 | 2443 | 1. **Estimating the contamination factor’s distribution in unsupervised anomaly detection.** ICML, 2022. [paper](https://proceedings.mlr.press/v202/perini23a.html) 2444 | 2445 | *Lorenzo Perini, Paul-Christian Bürkner, and Arto Klami.* 2446 | 2447 | 1. **F1-EV Score: Measuring the likelihood of estimating a good decision threshold for semi-supervised anomaly detection.** ICASSP, 2024. [paper](https://arxiv.org/abs/2312.09143) 2448 | 2449 | *Kevin Wilkinghoff and Keisuke Imoto.* 2450 | 2451 | 1. **Can untrained neural networks detect anomalies?** TII, 2024. [paper](https://ieeexplore.ieee.org/document/10391266) 2452 | 2453 | *Seunghyoung Ryu, Yonggyun Yu, and Hogeon Seo.* 2454 | 2455 | 1. **Interleaving one-class and weakly-supervised models with adaptive thresholding for unsupervised video anomaly detection.** arXiv, 2024. [paper](https://arxiv.org/abs/2401.13551) 2456 | 2457 | *Yongwei Nie, Hao Huang, Chengjiang Long, Qing Zhang, Pradipta Maji, and Hongmin Cai.* 2458 | 2459 | 1. **Reimagining anomalies: What if anomalies were normal?** arXiv, 2024. [paper](https://arxiv.org/abs/2402.14469) 2460 | 2461 | *Philipp Liznerski, Saurabh Varshneya, Ece Calikus, Sophie Fellenz, and Marius Kloft.* 2462 | 2463 | 1. **Long-tailed anomaly detection with learnable class names.** CVPR, 2024. [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Ho_Long-Tailed_Anomaly_Detection_with_Learnable_Class_Names_CVPR_2024_paper.html) 2464 | 2465 | *Chih-Hui Ho, Kuan-Chuan Peng, and Nuno Vasconcelos.* 2466 | 2467 | 1. **Anomaly detection by context contrasting.** arXiv, 2024. [paper](https://arxiv.org/abs/2405.18848) 2468 | 2469 | *Alain Ryser, Thomas M. Sutter, Alexander Marx, and Julia E. Vogt.* 2470 | 2471 | 1. **From zero to hero: Cold-start anomaly detection.** ACL, 2024. [paper](https://arxiv.org/abs/2405.20341) 2472 | 2473 | *Tal Reiss, George Kour, Naama Zwerdling, Ateret Anaby-Tavor, and Yedid Hoshen.* 2474 | 2475 | 1. **Contamination-resilient anomaly detection via adversarial learning on partially-observed normal and anomalous data.** ICML, 2024. [paper](https://openreview.net/forum?id=6PTiCmGcNx) 2476 | 2477 | *Wenxi Lv, Qinliang Su, Hai Wan, Hongteng Xu, and Wenchao Xu.* 2478 | 2479 | 1. **Self-cognitive denoising in the presence of multiple noisy label sources.** ICML, 2024. [paper](https://openreview.net/forum?id=CG44RLeXt1) 2480 | 2481 | *Yixuan Sun, Yalin Zhang, Bin Han, Longfei Li, and Jun Zhou.* 2482 | 2483 | 1. **Anomaly score: Evaluating generative models and individual generated images based on complexity and vulnerability.** CVPR, 2024. [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Hwang_Anomaly_Score_Evaluating_Generative_Models_and_Individual_Generated_Images_based_CVPR_2024_paper.html) 2484 | 2485 | *Jaehui Hwang, Junghyuk Lee, and Jong-Seok Lee.* 2486 | 2487 | 1. **RTOD: Efficient outlier detection with ray tracing cores.** TKDE, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10663704) 2488 | 2489 | *Ziming Wang, Kai Zhang, Yangming Lv, Yinglong Wang, Zhigang Zhao, Zhenying He, Yinan Jing, and X. Sean Wang.* 2490 | 2491 | ### [Domain Adaptation](#content) 2492 | 1. **Few-shot domain-adaptive anomaly detection for cross-site brain imagess.** TPAMI, 2022. [paper](https://ieeexplore.ieee.org/document/9606561) 2493 | 2494 | *Jianpo Su, Hui Shen, Limin Peng, and Dewen Hu.* 2495 | 2496 | 1. **Registration based few-shot anomaly detection.** ECCV, 2021. [paper](https://link.springer.com/chapter/10.1007/978-3-031-20053-3_18) 2497 | 2498 | *Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael Spratling, and Yanfeng Wang.* 2499 | 2500 | 1. **Learning unsupervised metaformer for anomaly detection.** CVPR, 2021. [paper](https://openaccess.thecvf.com/content/ICCV2021/html/Wu_Learning_Unsupervised_Metaformer_for_Anomaly_Detection_ICCV_2021_paper.html) 2501 | 2502 | *Jhih-Ciang Wu, Dingjie Chen, Chiou-Shann Fuh, and Tyng-Luh Liu.* 2503 | 2504 | 1. **Generic and scalable framework for automated time-series anomaly detection.** KDD, 2019. [paper](https://dl.acm.org/doi/10.1145/2783258.2788611) 2505 | 2506 | *Nikolay Laptev, Saeed Amizadeh, and Ian Flint.* 2507 | 2508 | 1. **Transfer learning for anomaly detection through localized and unsupervised instance selection.** AAAI, 2020. [paper](https://ojs.aaai.org/index.php/AAAI/article/view/6068) 2509 | 2510 | *Vincent Vercruyssen, Wannes Meert, and Jesse Davis.* 2511 | 2512 | 1. **FewSome: Few shot anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2301.06957) 2513 | 2514 | *Niamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor, and Kathleen M. Curran.* 2515 | 2516 | 1. **Cross-domain video anomaly detection without target domain adaptation.** WACV, 2023. [paper](https://openaccess.thecvf.com/content/WACV2023/html/Aich_Cross-Domain_Video_Anomaly_Detection_Without_Target_Domain_Adaptation_WACV_2023_paper.html) 2517 | 2518 | *Abhishek Aich, Kuanchuan Peng, and Amit K. Roy-Chowdhury.* 2519 | 2520 | 1. **Zero-shot anomaly detection without foundation models.** arXiv, 2023. [paper](https://arxiv.org/abs/2302.07849) 2521 | 2522 | *Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, and Stephan Mandt.* 2523 | 2524 | 1. **Pushing the limits of fewshot anomaly detection in industry vision: A graphcore.** ICLR, 2023. [paper](https://openreview.net/forum?id=xzmqxHdZAwO) 2525 | 2526 | *Guoyang Xie, Jinbao Wang, Jiaqi Liu, Yaochu Jin, and Feng Zheng.* 2527 | 2528 | 1. **Meta-learning for robust anomaly detection.** AISTATS, 2023. [paper](https://proceedings.mlr.press/v206/kumagai23a.html) 2529 | 2530 | *Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, and Yasuhiro Fujiwara.* 2531 | 2532 | 1. **OneShotSTL: One-shot seasonal-trend decomposition for online time series anomaly detection and forecasting.** arXiv, 2023. [paper](https://arxiv.org/abs/2304.01506) 2533 | 2534 | *Xiao He, Ye Li, Jian Tan, Bin Wu, and Feifei Li.* 2535 | 2536 | 1. **Context-aware domain adaptation for time series anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2304.07453) 2537 | 2538 | *Kwei-Herng Lai, Lan Wang, Huiyuan Chen, Kaixiong Zhou, Fei Wang, Hao Yang, and Xia Hu.* 2539 | 2540 | 1. **MetaGAD: Learning to meta transfer for few-shot graph anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2305.10668) 2541 | 2542 | *Xiongxiao Xu, Kaize Ding, Canyu Chen, and Kai Shu.* 2543 | 2544 | 1. **A zero-/few-shot anomaly classification and segmentation method for CVPR 2023 VAND workshop challenge tracks 1&2: 1st place on zero-shot AD and 4th place on few-shot AD.** arXiv, 2023. [paper](https://arxiv.org/abs/2305.17382) 2545 | 2546 | *Xuhai Chen, Yue Han, and Jiangning Zhang.* 2547 | 2548 | 1. **Winning solution for the CVPR2023 visual anomaly and novelty detection challenge: Multimodal prompting for data-centric anomaly detection.** CVPR, 2023. [paper](https://arxiv.org/abs/2306.09067) 2549 | 2550 | *Yunkang Cao, Xiaohao Xu, Chen Sun, Yuqi Cheng, Liang Gao, and Weiming Shen.* 2551 | 2552 | 1. **Zero-shot anomaly detection with pre-trained segmentation models.** VAND, 2023. [paper](https://arxiv.org/abs/2306.09269) 2553 | 2554 | *Matthew Baugh, James Batten, Johanna P. Müller, and Bernhard Kainz.* 2555 | 2556 | 1. **Optimizing PatchCore for few/many-shot anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2307.10792) 2557 | 2558 | *João Santos, Triet Tran, and Oliver Rippel.* 2559 | 2560 | 1. **Multi-scale memory comparison for zero-/few-shot anomaly detection.** CVPR, 2023. [paper](https://arxiv.org/abs/2308.04789) 2561 | 2562 | *Chaoqin Huang, Aofan Jiang, Ya Zhang, and Yanfeng Wang.* 2563 | 2564 | 1. **AutoML for outlier detection with optimal Ttransport distances.** IJCAI, 2023. [paper](https://www.ijcai.org/proceedings/2023/843) 2565 | 2566 | *Prabhant Singh and Joaquin Vanschoren.* 2567 | 2568 | 1. **AutoML for outlier detection with optimal Ttransport distances.** ICCV, 2023. [paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Fang_FastRecon_Few-shot_Industrial_Anomaly_Detection_via_Fast_Feature_Reconstruction_ICCV_2023_paper.pdf) 2569 | 2570 | *Zheng Fang, Xiaoyang Wang, Haocheng Li, Jiejie Liu, Qiugui Hu, and Jimin Xiao.* 2571 | 2572 | 1. **Tight rates in supervised outlier transfer learning.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.04686) 2573 | 2574 | *Mohammadreza M. Kalan and Samory Kpotufe.* 2575 | 2576 | 1. **Few-shot anomaly detection with adversarial loss for robust feature representations.** BMVC, 2023. [paper](https://proceedings.bmvc2023.org/202/) 2577 | 2578 | *Jae Young Lee, Wonjun Lee, Jaehyun Choi, Yongkwi Lee, Young Seog Yoon, and Samory Kpotufe.* 2579 | 2580 | 1. **When model meets new normals: Test-time adaptation for unsupervised time-series anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2312.11976) 2581 | 2582 | *Dongmin Kim, Sunghyun Park, and Jaegul Choo.* 2583 | 2584 | 1. **Few shot part segmentation reveals compositional logic for industrial anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2312.13783) 2585 | 2586 | *Soopil Kim, Sion An, Philip Chikontwe, Myeongkyun Kang, Ehsan Adeli, Kilian M. Pohl, and Sanghyun Park.* 2587 | 2588 | 1. **METER: A dynamic concept adaptation framework for online anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2312.16831) 2589 | 2590 | *Jiaqi Zhu, Shaofeng Cai, Fang Deng, Beng Chin Ooi, and Wenqiao Zhang.* 2591 | 2592 | 1. **Zero-shot versus many-shot: Unsupervised texture anomaly detection.** WACV, 2023. [paper](https://openaccess.thecvf.com/content/WACV2023/html/Aota_Zero-Shot_Versus_Many-Shot_Unsupervised_Texture_Anomaly_Detection_WACV_2023_paper.html) 2593 | 2594 | *Toshimichi Aota, Lloyd Teh Tzer Tong, and Takayuki Okatani.* 2595 | 2596 | 1. **Anomaly detection with domain adaptation.** CVPR, 2023. [paper](https://openaccess.thecvf.com/content/CVPR2023W/VAND/html/Yang_Anomaly_Detection_With_Domain_Adaptation_CVPRW_2023_paper.html) 2597 | 2598 | *Ziyi Yang, Iman Soltani, and Eric Darve.* 2599 | 2600 | 1. **MuSc: Zero-shot anomaly classification and segmentation by mutual scoring of the unlabeled images.** ICLR, 2024. [paper](https://openreview.net/forum?id=AHgc5SMdtd) 2601 | 2602 | *Xurui Li, Ziming Huang, Feng Xue, and Yu Zhou.* 2603 | 2604 | 1. **Unified entropy optimization for open-set test-time adaptation.** CVPR, 2024. [paper](https://arxiv.org/abs/2404.06065) 2605 | 2606 | *Zhengqing Gao, Xuyao Zhang, and Chenglin Liu.* 2607 | 2608 | 1. **Deep domain-adversarial anomaly detection with robust one-class transfer learning.** KBS, 2024. [paper](https://www.sciencedirect.com/science/article/abs/pii/S0950705124008591) 2609 | 2610 | *Jingkai Chi and Zhizhong Mao.* 2611 | 2612 | 1. **AdaCLIP: Adapting CLIP with hybrid learnable prompts for zero-shot anomaly detection.** ECCV, 2024. [paper](https://arxiv.org/abs/2407.15795) 2613 | 2614 | *Yunkang Cao, Jiangning Zhang, Luca Frittoli, Yuqi Cheng, Weiming Shen, and Giacomo Boracchi.* 2615 | 2616 | 1. **Graph anomaly detection with few labels: A data-centric approach.** KDD, 2024. [paper](https://dl.acm.org/doi/abs/10.1145/3637528.3671929) 2617 | 2618 | *Xiaoxiao Ma, Ruikun Li, Fanzhen Liu, Kaize Ding, Jian Yang, and Jia Wu.* 2619 | 2620 | 1. **AA-CLIP: Enhancing zero-shot anomaly detection via anomaly-aware CLIP.** CVPR, 2025. [paper](https://arxiv.org/abs/2503.06661) 2621 | 2622 | *Wenxin Ma, Xu Zhang, Qingsong Yao, Fenghe Tang, Chenxu Wu, Yingtai Li, Rui Yan, Zihang Jiang, and S.Kevin Zhou.* 2623 | 2624 | 1. **TailedCore: Few-shot sampling for unsupervised long-tail noisy anomaly detection.** CVPR, 2025. [paper](https://arxiv.org/abs/2504.02775) 2625 | 2626 | *Yoon Gyo Jung, Jaewoo Park, Jaeho Yoon, Kuan-Chuan Peng, Wonchul Kim, Andrew Beng Jin Teoh, and Octavia Camps* 2627 | 2628 | 1. **Bayesian prompt flow learning for zero-shot anomaly detection.** CVPR, 2025. [paper](https://arxiv.org/abs/2503.10080) 2629 | 2630 | *Zhen Qu, Xian Tao, Xinyi Gong, Shichen Qu, Qiyu Chen, Zhengtao Zhang, Xingang Wang, and Guiguang Ding.* 2631 | 2632 | ### [Loss Function](#content) 2633 | 1. **Detecting regions of maximal divergence for spatio-temporal anomaly detection.** TPAMI, 2018. [paper](https://ieeexplore.ieee.org/abstract/document/8352745) 2634 | 2635 | *Björn Barz, Erik Rodner, Yanira Guanche Garcia, and Joachim Denzler.* 2636 | 2637 | 1. **Convex formulation for learning from positive and unlabeled data.** ICML, 2015. [paper](https://dl.acm.org/doi/10.5555/3045118.3045266) 2638 | 2639 | *Marthinus Christoffel Du Plessis, Gang Niu, and Masashi Sugiyama.* 2640 | 2641 | 1. **Anomaly detection with score distribution discrimination.** KDD, 2023. [paper](https://arxiv.org/abs/2306.14403) 2642 | 2643 | *Minqi Jiang, Songqiao Han, and Hailiang Huang.* 2644 | 2645 | 1. **AdaFocal: Calibration-aware adaptive focal loss.** NIPS, 2022. [paper](https://openreview.net/forum?id=CoMOKHYWf2) 2646 | 2647 | *Arindam Ghosh, Arindam_Ghosh, and Thomas Schaaf, Matthew R. Gormley.* 2648 | 2649 | 1. **DSV: An alignment validation loss for self-supervised outlier model selection.** arXiv, 2023. [paper](https://arxiv.org/abs/2307.06534) 2650 | 2651 | *Jaemin Yoo, Yue Zhao, Lingxiao Zhao, and Leman Akoglu.* 2652 | 2653 | 1. **Simple and effective out-of-distribution detection via cosine-based softmax loss.** ICCV, 2023. [paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Noh_Simple_and_Effective_Out-of-Distribution_Detection_via_Cosine-based_Softmax_Loss_ICCV_2023_paper.pdf) 2654 | 2655 | *SoonCheol Noh, DongEon Jeong, and Jee-Hyong Lee.* 2656 | 2657 | 1. **Temporal shift - multi-objective loss function for improved anomaly fall detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2311.02863) 2658 | 2659 | *Stefan Denkovski, Shehroz S. Khan, and Alex Mihailidis.* 2660 | 2661 | 1. **MOODv2: Masked image modeling for out-of-distribution detection.** arXiv, 2024. [paper](https://arxiv.org/abs/2401.02611) 2662 | 2663 | *Jingyao Li, Pengguang Chen, Shaozuo Yu, Shu Liu, and Jiaya Jia.* 2664 | 2665 | 1. **MOODv2: Masked image modeling for out-of-distribution detection.** ICLR, 2025. [paper](https://arxiv.org/abs/2503.02424) 2666 | 2667 | *Wei Luo, Yunkang Cao, Haiming Yao, Xiaotian Zhang, Jianan Lou, Yuqi Cheng, Weiming Shen, and Wenyong Yu.* 2668 | 2669 | ### [Model Selection](#content) 2670 | 1. **Automatic unsupervised outlier model selection.** NIPS, 2021. [paper](https://proceedings.neurips.cc/paper/2021/hash/23c894276a2c5a16470e6a31f4618d73-Abstract.html) 2671 | 2672 | *Yue Zhao, Ryan Rossi, and Leman Akoglu.* 2673 | 2674 | 1. **Toward unsupervised outlier model selection.** ICDM, 2022. [paper](https://www.andrew.cmu.edu/user/yuezhao2/papers/22-icdm-elect.pdf) 2675 | 2676 | *Yue Zhao, Sean Zhang, and Leman Akoglu.* 2677 | 2678 | 1. **Unsupervised model selection for time-series anomaly detection.** ICLR, 2023. [paper](https://openreview.net/forum?id=gOZ_pKANaPW) 2679 | 2680 | *Mononito Goswami, Cristian Ignacio Challu, Laurent Callot, Lenon Minorics, and Andrey Kan.* 2681 | 2682 | 1. **Fast Unsupervised deep outlier model selection with hypernetworks.** arXiv, 2023. [paper](https://arxiv.org/abs/2307.10529) 2683 | 2684 | *Xueying Ding, Yue Zhao, and Leman Akoglu.* 2685 | 2686 | 1. **ADGym: Design choices for deep anomaly detection.** NIPS, 2023. [paper](https://openreview.net/forum?id=9CKx9SsSSc) 2687 | 2688 | *Minqi Jiang, Chaochuan Hou, Ao Zheng, Songqiao Han,Hailiang Huang, Qingsong Wen, Xiyang Hu, and Yue Zha.* 2689 | 2690 | 1. **Model selection of anomaly detectors in the absence of labeled validation data.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.10461) 2691 | 2692 | *Clement Fung, Chen Qiu, Aodong Li, and Maja Rudolph.* 2693 | 2694 | 1. **TransNAS-TSAD: Harnessing transformers for multi-objective neural architecture search in time series anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2311.18061) 2695 | 2696 | *Ijaz Ul Haq and Byung Suk Lee.* 2697 | 2698 | 1. **HyperMix: Out-of-distribution detection and classification in few-shot settings.** WACV, 2024. [paper](https://openaccess.thecvf.com/content/WACV2024/html/Mehta_HyperMix_Out-of-Distribution_Detection_and_Classification_in_Few-Shot_Settings_WACV_2024_paper.html) 2699 | 2700 | *Nikhil Mehta, Kevin J Liang, Jing Huang, Fujen Chu, Li Yin, and Tal Hassner.* 2701 | 2702 | 1. **A general framework for the assessment of detectors of anomalies in time series.** TII, 2024. [paper](https://ieeexplore.ieee.org/document/10583949) 2703 | 2704 | *Andriy Enttsel, Silvia Onofri, Alex Marchioni, Mauro Mangia, Gianluca Setti, and Riccardo Rovatti.* 2705 | 2706 | 1. **MetaOOD: Automatic selection of OOD detection models.** KDD, 2024. [paper](https://arxiv.org/abs/2410.03074) 2707 | 2708 | *Yuehan Qin, Yichi Zhang, Yi Nian, Xueying Ding, and Yue Zhao.* 2709 | 2710 | ### [Knowledge Distillation](#content) 2711 | 1. **Anomaly detection via reverse distillation from one-class embedding.** CVPR, 2022. [paper](https://openaccess.thecvf.com/content/CVPR2022/html/Deng_Anomaly_Detection_via_Reverse_Distillation_From_One-Class_Embedding_CVPR_2022_paper.html) 2712 | 2713 | *Hanqiu Deng and Xingyu Li.* 2714 | 2715 | 1. **Multiresolution knowledge distillation for anomaly detection.** CVPR, 2021. [paper](https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html) 2716 | 2717 | *Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H. Rohban, and Hamid R. Rabiee.* 2718 | 2719 | 1. **Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings.** CVPR, 2020. [paper](https://openaccess.thecvf.com/content_CVPR_2020/html/Bergmann_Uninformed_Students_Student-Teacher_Anomaly_Detection_With_Discriminative_Latent_Embeddings_CVPR_2020_paper.html) 2720 | 2721 | *Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger.* 2722 | 2723 | 1. **Reconstructed student-teacher and discriminative networks for anomaly detection.** IROS, 2022. [paper](https://arxiv.org/abs/2210.07548) 2724 | 2725 | *Shinji Yamada, Satoshi Kamiya, and Kazuhiro Hotta.* 2726 | 2727 | 1. **Anomaly detection via reverse distillation from one-class embedding.** CVPR, 2022. [paper](https://openaccess.thecvf.com/content/CVPR2022/html/Deng_Anomaly_Detection_via_Reverse_Distillation_From_One-Class_Embedding_CVPR_2022_paper.html) 2728 | 2729 | *Hanqiu Deng and Xingyu Li.* 2730 | 2731 | 1. **DeSTSeg: Segmentation guided denoising student-teacher for anomaly detection.** CVPR, 2023. [paper](https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_DeSTSeg_Segmentation_Guided_Denoising_Student-Teacher_for_Anomaly_Detection_CVPR_2023_paper.html) 2732 | 2733 | *Xuan Zhang, Shiyu Li, Xi Li, Ping Huang, Jiulong Shan, and Ting Chen.* 2734 | 2735 | 1. **Asymmetric student-teacher networks for industrial anomaly detection.** WACV, 2023. [paper](https://openaccess.thecvf.com/content/WACV2023/html/Rudolph_Asymmetric_Student-Teacher_Networks_for_Industrial_Anomaly_Detection_WACV_2023_paper.html) 2736 | 2737 | *Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt.* 2738 | 2739 | 1. **In-painting radiography images for unsupervised anomaly detection.** CVPR, 2023. [paper](https://arxiv.org/abs/2111.13495) 2740 | 2741 | *Tiange Xiang, Yongyi Lu, Alan L. Yuille, Chaoyi Zhang, Weidong Cai, and Zongwei Zhou.* 2742 | 2743 | 1. **Self-distilled masked auto-encoders are efficient video anomaly detectors.** CVPR, 2024. [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Ristea_Self-Distilled_Masked_Auto-Encoders_are_Efficient_Video_Anomaly_Detectors_CVPR_2024_paper.html) 2744 | 2745 | *Nicolae-Catalin Ristea, Florinel-Alin Croitoru, Radu Tudor Ionescu, Marius Popescu, Fahad Shahbaz Khan, and Mubarak Shah.* 2746 | 2747 | 1. **Contextual affinity distillation for image anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2307.03101) 2748 | 2749 | *Jie Zhang, Masanori Suganuma, and Takayuki Okatani.* 2750 | 2751 | 1. **Reinforcement learning by guided safe exploration.** ECAI, 2023. [paper](https://arxiv.org/abs/2307.14316) 2752 | 2753 | *Qisong Yang, Thiago D. Simão, Nils Jansen, Simon H. Tindemans, and Matthijs T. J. Spaan.* 2754 | 2755 | 1. **Prior knowledge guided network for video anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2309.01682) 2756 | 2757 | *Zhewen Deng, Dongyue Chen, and Shizhuo Deng.* 2758 | 2759 | 1. **Asymmetric Student-Teacher Networks for Industrial Anomaly Detection.** WACV, 2023. [paper](https://openaccess.thecvf.com/content/WACV2023/html/Rudolph_Asymmetric_Student-Teacher_Networks_for_Industrial_Anomaly_Detection_WACV_2023_paper.html) 2760 | 2761 | *Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt.* 2762 | 2763 | 1. **Attention-conditioned augmentations for self-supervised anomaly detection and localization.** AAAI, 2023. [paper](https://ojs.aaai.org/index.php/AAAI/article/view/26720) 2764 | 2765 | *Behzad Bozorgtabar and Dwarikanath Mahapatra.* 2766 | 2767 | 1. **EfficientAD: Accurate visual anomaly detection at millisecond-level latencies.** WACV, 2024. [paper](https://openaccess.thecvf.com/content/WACV2024/html/Batzner_EfficientAD_Accurate_Visual_Anomaly_Detection_at_Millisecond-Level_Latencies_WACV_2024_paper.html) 2768 | 2769 | *Kilian Batzner, Lars Heckler, and Rebecca König.* 2770 | 2771 | 1. **Remembering normality: Memory-guided knowledge distillation for unsupervised anomaly detection.** ICCV, 2023. [paper](https://openaccess.thecvf.com/content/ICCV2023/html/Gu_Remembering_Normality_Memory-guided_Knowledge_Distillation_for_Unsupervised_Anomaly_Detection_ICCV_2023_paper.html) 2772 | 2773 | *Zhihao Gu, Liang Liu, Xu Chen, Ran Yi, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Annan Shu, Guannan Jiang, and Lizhuang Ma.* 2774 | 2775 | 1. **Revisiting reverse distillation for anomaly detection.** CVPR, 2023. [paper](https://openaccess.thecvf.com/content/CVPR2023/html/Tien_Revisiting_Reverse_Distillation_for_Anomaly_Detection_CVPR_2023_paper.html) 2776 | 2777 | *Tran Dinh Tien, Anh Tuan Nguyen, Nguyen Hoang Tran, Ta Duc Huy, Soan T.M. Duong, Chanh D. Tr. Nguyen, and Steven Q. H. Truong.* 2778 | 2779 | 1. **Dual-student knowledge distillation networks for unsupervised anomaly detection.** arXiv, 2024. [paper](https://arxiv.org/abs/2402.00448) 2780 | 2781 | *Liyi Yao and Shaobing Gao.* 2782 | 2783 | 1. **Structural teacher-student normality learning for multi-class anomaly detection and localization.** arXiv, 2024. [paper](https://arxiv.org/abs/2402.17091) 2784 | 2785 | *Hanqiu Deng and Xingyu Li.* 2786 | 2787 | 1. **Score distillation for anomaly detection.** KBS, 2024. [paper](https://www.sciencedirect.com/science/article/abs/pii/S0950705124004763) 2788 | 2789 | *Jeongmin Hong and Seokho Kang.* 2790 | 2791 | 1. **Dual-modeling decouple distillation for unsupervised anomaly detection.** MM, 2024. [paper](https://arxiv.org/abs/2408.03888) 2792 | 2793 | *Xinyue Liu, Jianyuan Wang, Biao Leng, and Shuo Zhang.* 2794 | 2795 | 1. **Filter or compensate: Towards invariant representation from distribution shift for anomaly detection.** AAAI, 2025. [paper](https://arxiv.org/abs/2412.10115) 2796 | 2797 | *Zining Chen, Xingshuang Luo, Weiqiu Wang, Zhicheng Zhao, Fei Su, and Aidong Men.* 2798 | 2799 | 1. **Scale-aware contrastive reverse distillation for unsupervised medical anomaly detection.** ICLR, 2025. [paper](https://openreview.net/forum?id=HNOo4UNPBF) 2800 | 2801 | *Chunlei Li, Yilei Shi, Jingliang Hu, Xiao Xiang Zhu, and Lichao Mou.* 2802 | 2803 | ### [Data Augmentation](#content) 2804 | 1. **Interpretable, multidimensional, multimodal anomaly detection with negative sampling for detection of device failure.** ICML, 2020. [paper](https://proceedings.mlr.press/v119/sipple20a.html) 2805 | 2806 | *John Sipple.* 2807 | 2808 | 1. **Doping: Generative data augmentation for unsupervised anomaly detection with GAN.** ICDM, 2018. [paper](https://ieeexplore.ieee.org/abstract/document/8594955) 2809 | 2810 | *Swee Kiat Lim, Yi Loo, Ngoc-Trung Tran, Ngai-Man Cheung, Gemma Roig, and Yuval Elovici.* 2811 | 2812 | 1. **Detecting anomalies within time series using local neural transformations.** arXiv, 2022. [paper](https://arxiv.org/abs/2202.03944) 2813 | 2814 | *Tim Schneider, Chen Qiu, Marius Kloft, Decky Aspandi Latif, Steffen Staab, Stephan Mandt, and Maja Rudolph.* 2815 | 2816 | 1. **Deep anomaly detection using geometric transformations.** NIPS, 2018. [paper](https://papers.nips.cc/paper/2018/hash/5e62d03aec0d17facfc5355dd90d441c-Abstract.html) 2817 | 2818 | *Izhak Golan and Ran El-Yaniv.* 2819 | 2820 | 1. **Locally varying distance transform for unsupervised visual anomaly detection.** ECCV, 2022. [paper](https://link.springer.com/chapter/10.1007/978-3-031-20056-4_21) 2821 | 2822 | *Wenyan Lin, Zhonghang Liu, and Siying Liu.* 2823 | 2824 | 1. **DAGAD: Data augmentation for graph anomaly detection.** ICDM, 2022. [paper](https://arxiv.org/abs/2210.09766) 2825 | 2826 | *Fanzhen Liu, Xiaoxiao Ma, Jia Wu, Jian Yang, Shan Xue†, Amin Beheshti, Chuan Zhou, Hao Peng, Quan Z. Sheng, and Charu C. Aggarwal.* 2827 | 2828 | 1. **Unsupervised dimension-contribution-aware embeddings transformation for anomaly detection.** KBS, 2022. [paper](https://www.sciencedirect.com/science/article/pii/S0950705122013053) 2829 | 2830 | *Liang Xi, Chenchen Liang, Han Liu, and Ao Li.* 2831 | 2832 | 1. **No shifted augmentations (NSA): Compact distributions for robust self-supervised anomaly detection.** WACV, 2023. [paper](https://openaccess.thecvf.com/content/WACV2023/html/Yousef_No_Shifted_Augmentations_NSA_Compact_Distributions_for_Robust_Self-Supervised_Anomaly_WACV_2023_paper.html) 2833 | 2834 | *Mohamed Yousef, Marcel Ackermann, Unmesh Kurup, and Tom Bishop.* 2835 | 2836 | 1. **End-to-end augmentation hyperparameter tuning for self-supervised anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2306.12033) 2837 | 2838 | *Jaemin Yoo, Lingxiao Zhao, and Leman Akoglu.* 2839 | 2840 | 1. **Data augmentation is a hyperparameter: Cherry-picked self-supervision for unsupervised anomaly detection is creating the illusion of success.** TMLR, 2023. [paper](https://openreview.net/forum?id=HyzCuCV1jH) 2841 | 2842 | *Jaemin Yoo, Tiancheng Zhao, and Leman Akoglu.* 2843 | 2844 | 1. **Diverse data augmentation with diffusions for effective test-time prompt tuning.** ICCV, 2023. [paper](https://arxiv.org/abs/2308.06038) 2845 | 2846 | *Chunmei Feng, Kai Yu, Yong Liu, Salman Khan, and Wangmeng Zuo.* 2847 | 2848 | 1. **GraphPatcher: Mitigating degree bias for graph neural networks via test-time augmentation.** NIPS, 2023. [paper](https://neurips.cc/virtual/2023/poster/70390) 2849 | 2850 | *Mingxuan Ju, Tong Zhao, Wenhao Yu, Neil Shah, and Yanfang Ye.* 2851 | 2852 | 1. **Towards reliable AI model deployments: Multiple input mixup for out-of-distribution detection.** AAAI, 2024. [paper](https://arxiv.org/abs/2312.15514) 2853 | 2854 | *Dasol Choi and Dongbin Na.* 2855 | 2856 | 1. **Data augmentation for supervised graph outlier detection with latent diffusion models.** arXiv, 2023. [paper](https://arxiv.org/abs/2312.17679) 2857 | 2858 | *Kay Liu, Hengrui Zhang, Ziqing Hu, Fangxin Wang, and Philip S. Yu.* 2859 | 2860 | 1. **No shifted augmentations (NSA): Compact distributions for robust self-supervised anomaly detection.** WACV, 2023. [paper](https://openaccess.thecvf.com/content/WACV2023/html/Yousef_No_Shifted_Augmentations_NSA_Compact_Distributions_for_Robust_Self-Supervised_Anomaly_WACV_2023_paper.html) 2861 | 2862 | *Mohamed Yousef, Marcel Ackermann, Unmesh Kurup, and Tom Bishop.* 2863 | 2864 | 1. **What makes a good data augmentation for few-shot unsupervised image anomaly detection?** CVPR, 2023. [paper](https://openaccess.thecvf.com/content/CVPR2023W/VISION/html/Zhang_What_Makes_a_Good_Data_Augmentation_for_Few-Shot_Unsupervised_Image_CVPRW_2023_paper.html) 2865 | 2866 | *Lingrui Zhang, Shuheng Zhang, Guoyang Xie, Jiaqi Liu, Hua Yan, Jinbao Wang, Feng Zheng, and Yaochu Jin.* 2867 | 2868 | 1. **Consistency training with learnable data augmentation for graph anomaly detection with limited supervision.** ICLR, 2024. [paper](https://openreview.net/forum?id=elMKXvhhQ9) 2869 | 2870 | *Nan Chen, Zemin Liu, Bryan Hooi, Bingsheng He, Rizal Fathony, Jun Hu, and Jia Chen.* 2871 | 2872 | 1. **Rotation has two sides: Evaluating data augmentation for deep one-class classification.** ICLR, 2024. [paper](https://openreview.net/forum?id=Ad81awoBVS) 2873 | 2874 | *Guodong Wang, Yunhong Wang, Xiuguo Bao, and Di Huang.* 2875 | 2876 | ### [Outlier Exposure](#content) 2877 | 1. **Latent outlier exposure for anomaly detection with contaminated data.** ICML, 2022. [paper](https://arxiv.org/abs/2202.08088) 2878 | 2879 | *Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, and Stephan Mandt.* 2880 | 2881 | 1. **Deep anomaly detection with outlier exposure.** ICLR, 2019. [paper](https://openreview.net/forum?id=HyxCxhRcY7) 2882 | 2883 | *Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich.* 2884 | 2885 | 1. **A simple and effective baseline for out-of-distribution detection using abstention.** ICLR, 2021. [paper](https://openreview.net/forum?id=q_Q9MMGwSQu) 2886 | 2887 | *Sunil Thulasidasan, Sushil Thapa, Sayera Dhaubhadel, Gopinath Chennupati, Tanmoy Bhattacharya, and Jeff Bilmes.* 2888 | 2889 | 1. **Does your dermatology classifier know what it doesn’t know? Detecting the long-tail of unseen conditions.** Medical Image Analysis, 2022. [paper](https://www.sciencedirect.com/science/article/pii/S1361841521003194) 2890 | 2891 | *Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi, Aaron Loh, Vivek Natarajan, Basil Mustafa, Nick Pawlowski, Jan Freyberg, Yuan Liu, Zach Beaver, Nam Vo, Peggy Bui, Samantha Winter, Patricia MacWilliams, Greg S. Corrado, Umesh Telang, Yun Liu, Taylan Cemgil, Alan Karthikesalingam, Balaji Lakshminarayanan, and Jim Winkens.* 2892 | 2893 | 1. **OpenMix: Exploring outlier samples for misclassification detection.** CVPR, 2023. [paper](https://arxiv.org/abs/2303.17093) 2894 | 2895 | *Fei Zhu, Zhen Cheng, Xuyao Zhang, and Chenglin Liu.* 2896 | 2897 | 1. **VOS: Learning what you don't know by virtual outlier synthesis.** ICLR, 2023. [paper](https://openreview.net/forum?id=TW7d65uYu5M) 2898 | 2899 | *Xuefeng Du, Zhaoning Wang, Mu Cai, and Yixuan Li.* 2900 | 2901 | 1. **Deep anomaly detection under labeling budget constraints.** ICML, 2023. [paper](https://openreview.net/forum?id=VjopP4ejwB) 2902 | 2903 | *Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, and Maja Rudolph.* 2904 | 2905 | 1. **Pseudo outlier exposure for out-of-distribution detection using pretrained Transformers.** ACL, 2023. [paper](https://aclanthology.org/2023.findings-acl.95/) 2906 | 2907 | *Jaeyoung Kim, Kyuheon Jung, Dongbin Na, Sion Jang, Eunbin Park, and Sungchul Choi.* 2908 | 2909 | 1. **Harder synthetic anomalies to improve OOD detection in medical images.** arXiv, 2023. [paper](https://arxiv.org/abs/2308.01412) 2910 | 2911 | *Sergio Naval Marimont and Giacomo Tarroni.* 2912 | 2913 | 1. **AutoLog: A log sequence synthesis framework for anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2308.09324) 2914 | 2915 | *Yintong Huo, Yichen Li, Yuxin Su, Pinjia He, Zifan Xie, and Michael R. Lyu.* 2916 | 2917 | 1. **Non-parametric outlier synthesis.** ICLR, 2023. [paper](https://openreview.net/forum?id=JHklpEZqduQ) 2918 | 2919 | *Leitian Tao, Xuefeng Du, Jerry Zhu, and Yixuan Li.* 2920 | 2921 | 1. **Dream the impossible: Outlier imagination with diffusion models.** NIPS, 2023. [paper](https://openreview.net/forum?id=tnRboxQIec) 2922 | 2923 | *Xuefeng Du, Yiyou Sun, Xiaojin Zhu, and Yixuan Li.* 2924 | 2925 | 1. **On the powerfulness of textual outlier exposure for visual OOD detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.16492) 2926 | 2927 | *Sangha Park, Jisoo Mok, Dahuin Jung, Saehyung Lee, and Sungroh Yoon.* 2928 | 2929 | 1. **A coarse-to-fine pseudo-labeling (C2FPL) framework for unsupervised video anomaly detection.** WACV, 2024. [paper](https://arxiv.org/abs/2310.17650) 2930 | 2931 | *Anas Al-lahham, Nurbek Tastan, Zaigham Zaheer, and Karthik Nandakumar.* 2932 | 2933 | 1. **Diversified outlier exposure for out-of-distribution detection via informative extrapolation.** NIPS, 2023. [paper](https://openreview.net/forum?id=RuxBLfiEqI) 2934 | 2935 | *Jianing Zhu, Geng Yu, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, and Bo Han.* 2936 | 2937 | 1. **Out-of-distribution detection learning with unreliable out-of-distribution sources.** NIPS, 2023. [paper](https://openreview.net/forum?id=RuxBLfiEqI) 2938 | 2939 | *Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, and Bo Han.* 2940 | 2941 | 1. **NNG-Mix: Improving semi-supervised anomaly detection with pseudo-anomaly generation.** arXiv, 2023. [paper](https://arxiv.org/abs/2311.11961) 2942 | 2943 | *Hao Dong, Gaëtan Frusque, Yue Zhao, Eleni Chatzi, and Olga Fink.* 2944 | 2945 | 1. **Exploiting completeness and uncertainty of pseudo labels for weakly supervised video anomaly detection.** CVPR, 2023. [paper](https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Exploiting_Completeness_and_Uncertainty_of_Pseudo_Labels_for_Weakly_Supervised_CVPR_2023_paper.html) 2946 | 2947 | *Chen Zhang, Guorong Li, Yuankai Qi, Shuhui Wang, Laiyun Qing, Qingming Huang, and Ming-Hsuan Yang.* 2948 | 2949 | 1. **Generating anomalies for video anomaly detection with prompt-based feature mapping.** CVPR, 2023. [paper](https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Generating_Anomalies_for_Video_Anomaly_Detection_With_Prompt-Based_Feature_Mapping_CVPR_2023_paper.html) 2950 | 2951 | *Zuhao Liu, Xiaoming Wu, Dian Zheng, Kunyu Lin, and Weishi Zheng.* 2952 | 2953 | 1. **Generating anomalies for video anomaly detection with prompt-based feature mapping.** CVPR, 2023. [paper](https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Generating_Anomalies_for_Video_Anomaly_Detection_With_Prompt-Based_Feature_Mapping_CVPR_2023_paper.html) 2954 | 2955 | *Zuhao Liu, Xiaoming Wu, Dian Zheng, Kunyu Lin, and Weishi Zheng.* 2956 | 2957 | 1. **Text-guided variational image generation for industrial anomaly detection and segmentation.** CVPR, 2024. [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Lee_Text-Guided_Variational_Image_Generation_for_Industrial_Anomaly_Detection_and_Segmentation_CVPR_2024_paper.html) 2958 | 2959 | *Mingyu Lee and Jongwon Choi.* 2960 | 2961 | 1. **RealNet: A feature selection network with realistic synthetic anomaly for anomaly detection.** CVPR, 2024. [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_RealNet_A_Feature_Selection_Network_with_Realistic_Synthetic_Anomaly_for_CVPR_2024_paper.html) 2962 | 2963 | *Ximiao Zhang, Min Xu, and Xiuzhuang Zhou.* 2964 | 2965 | 1. **Negative label guided OOD detection with pretrained vision-language models.** ICLR, 2024. [paper](https://openreview.net/forum?id=xUO1HXz4an) 2966 | 2967 | *Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, and Bo Han.* 2968 | 2969 | 1. **Envisioning outlier exposure by large language models for out-of-distribution detection.** ICML, 2024. [paper](https://arxiv.org/abs/2406.00806) 2970 | 2971 | *Chentao Cao, Zhun Zhong, Zhanke Zhou, Yang Liu, Tongliang Liu, and Bo Han.* 2972 | 2973 | 1. **Towards open-world object-based anomaly detection via self-supervised outlier synthesis.** ECCV, 2024. [paper](https://arxiv.org/abs/2407.15763) 2974 | 2975 | *Brian K. S. Isaac-Medina, Yona Falinie A. Gaus, Neelanjan Bhowmik, and Toby P. Breckon.* 2976 | 2977 | 1. **Out-of-distribution detection with virtual outlier smoothing.** IJCV, 2024. [paper](https://link.springer.com/article/10.1007/s11263-024-02210-8) 2978 | 2979 | *Jun Nie, Yadan Luo, Shanshan Ye, Yonggang Zhang, Xinmei Tian, and Zhen Fang.* 2980 | 2981 | 1. **W-DOE: Wasserstein distribution-agnostic outlier exposure.** TPAMI, 2025. [paper](https://ieeexplore.ieee.org/document/10844561) 2982 | 2983 | *Qizhou Wang, Bo Han, Yang Liu, Chen Gong, Tongliang Liu, and Jiming Liu.* 2984 | 2985 | ### [Contrastive Learning](#content) 2986 | 1. **Graph anomaly detection via multi-scale contrastive learning networks with augmented view.** AAAI, 2023. [paper](https://arxiv.org/abs/2212.00535) 2987 | 2988 | *Jingcan Duan, Siwei Wang, Pei Zhang, En Zhu, Jingtao Hu, Hu Jin, Yue Liu, and Zhibin Dong.* 2989 | 2990 | 1. **Partial and asymmetric contrastive learning for out-of-distribution detection in long-tailed recognition.** ICML, 2022. [paper](https://proceedings.mlr.press/v162/wang22aq.html) 2991 | 2992 | *Haotao Wang, Aston Zhang, Yi Zhu, Shuai Zheng, Mu Li, Alex Smola, and Zhangyang Wang.* 2993 | 2994 | 1. **Focus your distribution: Coarse-to-fine non-contrastive learning for anomaly detection and localization.** ICME, 2022. [paper](https://ieeexplore.ieee.org/abstract/document/9859925) 2995 | 2996 | *Ye Zheng, Xiang Wang, Rui Deng, Tianpeng Bao, Rui Zhao, and Liwei Wu.* 2997 | 2998 | 1. **MGFN: Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2211.15098) 2999 | 3000 | *Yingxian Chen, Zhengzhe Liu, Baoheng Zhang, Wilton Fok, Xiaojuan Qi, and Yik-Chung Wu.* 3001 | 3002 | 1. **On the effectiveness of out-of-distribution data in self-supervised long-tail learning.** ICLR, 2023. [paper](https://openreview.net/forum?id=v8JIQdiN9Sh) 3003 | 3004 | *Jianhong Bai, Zuozhu Liu, Hualiang Wang, Jin Hao, Yang Feng, Huanpeng Chu, and Haoji Hu.* 3005 | 3006 | 1. **Hierarchical semantic contrast for scene-aware video anomaly detection.** CVPR, 2023. [paper](https://arxiv.org/abs/2303.13051) 3007 | 3008 | *Shengyang Sun and Xiaojin Gong.* 3009 | 3010 | 1. **Hierarchical semi-supervised contrastive learning for contamination-resistant anomaly detection.** ECCV, 2022. [paper](https://link.springer.com/chapter/10.1007/978-3-031-19806-9_7) 3011 | 3012 | *Gaoang Wang, Yibing Zhan, Xinchao Wang, Mingli Song, and Klara Nahrstedt.* 3013 | 3014 | 1. **Reconstruction enhanced multi-view contrastive learning for anomaly detection on attributed networks.** IJCAI, 2022. [paper](https://www.ijcai.org/proceedings/2022/0330) 3015 | 3016 | *Jiaqiang Zhang, Senzhang Wang, and Songcan Chen.* 3017 | 3018 | 1. **SimTS: Rethinking contrastive representation learning for time series forecasting.** arXiv, 2023. [paper](https://arxiv.org/abs/2303.18205) 3019 | 3020 | *Xiaochen Zheng, Xingyu Chen, Manuel Schürch, Amina Mollaysa, Ahmed Allam, and Michael Krauthammer.* 3021 | 3022 | 1. **CARLA: A self-supervised contrastive representation learning approach for time series anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2308.09296) 3023 | 3024 | *Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, and Mahsa Salehi.* 3025 | 3026 | 1. **Unilaterally aggregated contrastive learning with hierarchical augmentation for anomaly detection.** ICCV, 2023. [paper](https://arxiv.org/abs/2308.10155) 3027 | 3028 | *Guodong Wang, Yunhong Wang, Jie Qin, Dongming Zhang, Xiuguo Bao, and Di Huang.* 3029 | 3030 | 1. **Cross-domain graph anomaly detection via anomaly-aware contrastive alignment.** AAAI, 2023. [paper](https://ojs.aaai.org/index.php/AAAI/article/view/25591) 3031 | 3032 | *Qizhou Wang, Guansong Pang, Mahsa Salehi, Wray Buntine, and Christopher Leckie.* 3033 | 3034 | 1. **Robust fraud detection via supervised contrastive learning.** arXiv, 2023. [paper](https://arxiv.org/abs/2308.10055) 3035 | 3036 | *Vinay M.S., Shuhan Yuan, and Xintao Wu.* 3037 | 3038 | 1. **Understanding normalization in contrastive representation learning and out-of-distribution detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2312.15288) 3039 | 3040 | *Tai Le-Gia and Jaehyun Ahn.* 3041 | 3042 | 1. **Generating and reweighting dense contrastive patterns for unsupervised anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2312.15911) 3043 | 3044 | *Songmin Dai, Yifan Wu, Xiaoqiang Li, and Xiangyang Xue.* 3045 | 3046 | 1. **Mean-shifted contrastive loss for anomaly detection.** AAAI, 2023. [paper](https://ojs.aaai.org/index.php/AAAI/article/view/25309) 3047 | 3048 | *Tal Reiss and Yedid Hoshen.* 3049 | 3050 | 1. **Hierarchical semantic contrast for scene-aware video anomaly detection.** CVPR, 2023. [paper](https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Hierarchical_Semantic_Contrast_for_Scene-Aware_Video_Anomaly_Detection_CVPR_2023_paper.html) 3051 | 3052 | *Shengyang Sun and Xiaojin Gong.* 3053 | 3054 | 1. **Motif-aware Riemannian graph neural network with generative-contrastive learning.** AAAI, 2024. [paper](https://arxiv.org/abs/2401.01232) 3055 | 3056 | *Li Sun, Zhenhao Huang, Zixi Wang, Feiyang Wang, Hao Peng, and Philip Yu.* 3057 | 3058 | 1. **UAC-AD: Unsupervised adversarial contrastive learning for anomaly detection on multi-modal data in microservice systems.** TSC, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10552111) 3059 | 3060 | *Hongyi Liu, Xiaosong Huang, Mengxi Jia, Tong Jia, Jing Han, Ying Li, and Zhonghai Wu.* 3061 | 3062 | 1. **Soft contrastive learning for time series.** ICLR, 2024. [paper](https://openreview.net/forum?id=pAsQSWlDUf) 3063 | 3064 | *Seunghan Lee, Taeyoung Park, and Kibok Lee.* 3065 | 3066 | 1. **Model-guided contrastive fine-tuning for industrial anomaly detection.** CVPR, 2024. [paper](https://openaccess.thecvf.com/content/CVPR2024W/VAND/html/Artola_Model-guided_Contrastive_Fine-tuning_for_Industrial_Anomaly_Detection_CVPRW_2024_paper.html) 3067 | 3068 | *Aitor Artola, Yannis Kolodziej, Jean-Michel Morel, and Thibaud Ehret.* 3069 | 3070 | 1. **Universal novelty detection through adaptive contrastive learning.** CVPR, 2024. [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Mirzaei_Universal_Novelty_Detection_Through_Adaptive_Contrastive_Learning_CVPR_2024_paper.html) 3071 | 3072 | *Hossein Mirzaei, Mojtaba Nafez, Mohammad Jafari, Mohammad Bagher Soltani, Mohammad Azizmalayeri, Jafar Habibi, Mohammad Sabokrou, and Mohammad Hossein Rohban.* 3073 | 3074 | 1. **Regularized contrastive partial multi-view outlier detection.** MM, 2024. [paper](https://openreview.net/forum?id=9udVudwIw8) 3075 | 3076 | *Yijia Wang, Qianqian Xu, Yangbangyan Jiang, Siran Dai, and Qingming Huang* 3077 | 3078 | ### [Continual Learning](#content) 3079 | 1. **PANDA: Adapting pretrained features for anomaly detection and segmentation.** CVPR, 2021. 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[paper](https://ieeexplore.ieee.org/abstract/document/10412688) 3457 | 3458 | *Jiabin Liu, Huadong Wang, Hanyuan Hang, Shumin Ma, Xin Shen, and Yong Shi.* 3459 | 3460 | ### [Multi Modal](#content) 3461 | 1. **Multimodal industrial anomaly detection via hybrid fusion.** CVPR, 2023. [paper](https://arxiv.org/abs/2303.00601) 3462 | 3463 | *Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, and Chengjie Wang.* 3464 | 3465 | 1. **A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder.** ICRA, 2018. [paper](https://ieeexplore.ieee.org/abstract/document/8279425) 3466 | 3467 | *Daehyung Park, Yuuna Hoshi, and Charles C. Kemp.* 3468 | 3469 | 1. **EasyNet: An easy network for 3D industrial anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2307.13925) 3470 | 3471 | *Ruitao Chen, Guoyang Xie, Jiaqi Liu, Jinbao Wang, Ziqi Luo, Jinfan Wang, and Feng Zheng.* 3472 | 3473 | 1. **ADMire++: Explainable anomaly detection in the human brain via inductive learning on temporal multiplex networks.** ICML, 2023. [paper](https://openreview.net/pdf?id=t4H8acYudJ) 3474 | 3475 | *Ali Behrouz and Margo Seltzer.* 3476 | 3477 | 1. **Improving anomaly segmentation with multi-granularity cross-domain alignment.** arXiv, 2023. [paper](https://arxiv.org/abs/2308.08696) 3478 | 3479 | *Ji Zhang, Xiao Wu, Zhi-Qi Cheng, Qi He, and Wei Li.* 3480 | 3481 | 1. **Improving vision anomaly detection with the guidance of language modality.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.02821) 3482 | 3483 | *Dong Chen, Kaihang Pan, Guoming Wang, Yueting Zhuang, and Siliang Tang.* 3484 | 3485 | 1. **Debunking free fusion myth: Online multi-view anomaly detection with disentangled product-of-experts modeling.** MM, 2023. [paper](https://dl.acm.org/doi/abs/10.1145/3581783.3612487) 3486 | 3487 | *Hao Wang, Zhiqi Cheng, Jingdong Sun, Xin Yang, Xiao Wu, Hongyang Chen, and Yan Yang.* 3488 | 3489 | 1. **Multimodal industrial anomaly detection by crossmodal feature mapping.** CVPR, 2024. [paper]( 3490 | 1. **Multimodal industrial anomaly detection by crossmodal feature mapping.** arXiv, 2023. [paper](https://arxiv.org/abs/2312.04521) 3491 | 3492 | *Alex Costanzino, Pierluigi Zama Ramirez, Giuseppe Lisanti, and Luigi Di Stefano.*) 3493 | 3494 | *Alex Costanzino, Pierluigi Zama Ramirez, Giuseppe Lisanti, and Luigi Di Stefano.* 3495 | 3496 | ### [Optimal Transport](#content) 3497 | 1. **Prototype-oriented unsupervised anomaly detection for multivariate time series.** ICML, 2023. [paper](https://openreview.net/forum?id=3vO4lS6PuF) 3498 | 3499 | *Yuxin Li, Wenchao Chen, Bo Chen, Dongsheng Wang, Long Tian, and Mingyuan Zhou.* 3500 | 3501 | 1. **Weakly supervised anomaly detection via knowledge-data alignment.** WWW, 2024. [paper](https://arxiv.org/abs/2402.03785) 3502 | 3503 | *Haihong Zhao, Chenyi Zi, Yang Liu, Chen Zhang, Yan Zhou, and Jia Li.* 3504 | 3505 | ### [Causal Inference](#content) 3506 | 1. **Learning causality-inspired representation consistency for video anomaly detection.** ACM MM, 2023. [paper](https://arxiv.org/abs/2308.01537) 3507 | 3508 | *Yang Liu, Zhaoyang Xia, Mengyang Zhao, Donglai Wei, Yuzheng Wang, Liu Siao, Bobo Ju, Gaoyun Fang, Jing Liu, and Liang Song.* 3509 | 3510 | ### [Gaussian Process](#content) 3511 | 1. **Deep anomaly detection with deviation networks.** KDD, 2019. [paper](https://dl.acm.org/doi/10.1145/3292500.3330871) 3512 | 3513 | *Guansong Pang, Chunhua Shen, and Anton van den Hengel.* 3514 | 3515 | 1. **Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression.** CVPR, 2015. [paper](https://ieeexplore.ieee.org/document/7298909) 3516 | 3517 | *Kai-Wen Cheng and Yie-Tarng Chen, and Wen-Hsien Fang.* 3518 | 3519 | 1. **Multidimensional time series anomaly detection: A GRU-based Gaussian mixture variational autoencoder approach.** ACCV, 2018. [paper](http://proceedings.mlr.press/v95/guo18a.html) 3520 | 3521 | *Yifan Guo, Weixian Liao, Qianlong Wang, Lixing Yu, Tianxi Ji, and Pan Li.* 3522 | 3523 | 1. **Gaussian process regression-based video anomaly detection and localization with hierarchical feature representation.** TIP, 2015. [paper](https://ieeexplore.ieee.org/abstract/document/7271067) 3524 | 3525 | *Kaiwen Cheng, Yie-Tarng Chen, and Wen-Hsien Fang.* 3526 | 3527 | 1. **Adversarial anomaly detection using Gaussian priors and nonlinear anomaly scores.** ICDM, 2023. [paper](https://arxiv.org/abs/2310.18091) 3528 | 3529 | *Fiete Lüer, Tobias Weber, Maxim Dolgich, and Christian Böhm.* 3530 | 3531 | 1. **Invariant anomaly detection under distribution shifts: A causal perspective.** arXiv, 2023. [paper](https://arxiv.org/abs/2312.14329) 3532 | 3533 | *João B. S. Carvalho, Mengtao Zhang, Robin Geyer, Carlos Cotrini, and Joachim M. Buhmann.* 3534 | 3535 | ### [Multi Task](#content) 3536 | 1. **Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization.** IJCV, 2022. [paper](https://link.springer.com/article/10.1007/s11263-022-01578-9) 3537 | 3538 | *Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger.* 3539 | 3540 | 1. **Anomaly detection in video via self-supervised and multi-task learning.** CVPR, 2021. [paper](http://openaccess.thecvf.com/content/CVPR2021/html/Georgescu_Anomaly_Detection_in_Video_via_Self-Supervised_and_Multi-Task_Learning_CVPR_2021_paper.html) 3541 | 3542 | *Mariana-Iuliana Georgescu, Antonio Barbalau, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, and Mubarak Shah.* 3543 | 3544 | 1. **Detecting semantic anomalies.** AAAI, 2020. [paper](https://ojs.aaai.org/index.php/AAAI/article/view/5712) 3545 | 3546 | *Faruk Ahmed and Aaron Courville.* 3547 | 3548 | 1. **MGADN: A multi-task graph anomaly detection network for multivariate time series.** arXiv, 2022. [paper](https://arxiv.org/abs/2211.12141) 3549 | 3550 | *Weixuan Xiong and Xiaochen Sun.* 3551 | 3552 | 1. **Holistic representation learning for multitask trajectory anomaly detection.** WACV, 2023. [paper](https://arxiv.org/abs/2311.01851) 3553 | 3554 | *Alexandros Stergiou, Brent De Weerdt, and Nikos Deligiannis.* 3555 | 3556 | 1. **Multi-task learning based video anomaly detection with attention.** CVPR, 2023. [paper](https://openaccess.thecvf.com/content/CVPR2023W/VAND/html/Baradaran_Multi-Task_Learning_Based_Video_Anomaly_Detection_With_Attention_CVPRW_2023_paper.html) 3557 | 3558 | *Mohammad Baradaran and Robert Bergevin.* 3559 | 3560 | ### [Interpretability](#content) 3561 | 1. **Transparent anomaly detection via concept-based explanations.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.10702) 3562 | 3563 | *Laya Rafiee Sevyeri, Ivaxi Sheth, and Farhood Farahnak.* 3564 | 3565 | 1. **Towards self-interpretable graph-level anomaly detection.** NIPS, 2023. [paper](https://openreview.net/forum?id=SAzaC8f3cM) 3566 | 3567 | *Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, and Shirui Pan.* 3568 | 3569 | 1. **Explainable anomaly detection using masked latent generative modeling.** arXiv, 2023. [paper](https://arxiv.org/abs/2311.12550) 3570 | 3571 | *Daesoo Lee, Sara Malacarne, and Erlend Aune.* 3572 | 3573 | 1. **EVAL: Explainable video anomaly localization.** CVPR, 2023. [paper](https://openaccess.thecvf.com/content/CVPR2023/html/Singh_EVAL_Explainable_Video_Anomaly_Localization_CVPR_2023_paper.html) 3574 | 3575 | *Ashish Singh, Michael J. Jones, and Erik G. Learned-Miller.* 3576 | 3577 | 1. **Dissect black box: Interpreting for rule-based explanations in unsupervised anomaly detection.** NIPS, 2024. [paper](https://openreview.net/forum?id=h6o6qXLmHZ) 3578 | 3579 | *Yu Zhang, Ruoyu Li, Nengwu Wu, Qing Li, Xinhan Lin, Yang Hu, Tao Li, and Yong Jiang.* 3580 | 3581 | ### [Open Set](#content) 3582 | 1. **Anomaly heterogeneity learning for open-set supervised anomaly detection.** CVPR, 2024. [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Anomaly_Heterogeneity_Learning_for_Open-set_Supervised_Anomaly_Detection_CVPR_2024_paper.html) 3583 | 3584 | *Jiawen Zhu, Choubo Ding, Yu Tian, and Guansong Pang.* 3585 | 3586 | 1. **Open-set multivariate time-series anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.12294) 3587 | 3588 | *Thomas Lai, Thi Kieu Khanh Ho, and Narges Armanfard.* 3589 | 3590 | 1. **SSB: Simple but strong baseline for boosting performance of open-set semi-supervised learning.** ICCV, 2023. [paper](https://openaccess.thecvf.com/content/ICCV2023/html/Fan_SSB_Simple_but_Strong_Baseline_for_Boosting_Performance_of_Open-Set_ICCV_2023_paper.html) 3591 | 3592 | *Yue Fan, Anna Kukleva, Dengxin Dai, and Bernt Schiele.* 3593 | 3594 | ### [Neural Process](#content) 3595 | 1. **Semi-supervised anomaly detection via neural process.** TKDE, 2023. [paper](https://ieeexplore.ieee.org/abstract/document/10102264) 3596 | 3597 | *Fan Zhou, Guanyu Wang, Kunpeng Zhang, Siyuan Liu, and Ting Zhong.* 3598 | 3599 | 1. **Precursor-of-anomaly detection for irregular time series.** KDD, 2023. [paper](https://arxiv.org/abs/2306.15489) 3600 | 3601 | *Sheo Yon Jhin, Jaehoon Lee, and Noseong Park.* 3602 | 3603 | 1. **Pursuing feature separation based on neural collapse for out-of-distribution detection.** arXiv, 2024. [paper](https://arxiv.org/abs/2405.17816) 3604 | 3605 | *Yingwen Wu, Ruiji Yu, Xinwen Cheng, Zhengbao He, and Xiaolin Huang.* 3606 | 3607 | ### [Nonparametric Approach](#content) 3608 | 1. **Real-time nonparametric anomaly detection in high-dimensional settings.** TPAMI, 2021. [paper](https://ieeexplore.ieee.org/abstract/document/8976215/) 3609 | 3610 | *Mehmet Necip Kurt, Yasin Yılmaz, and Xiaodong Wang.* 3611 | 3612 | 1. **Neighborhood structure assisted non-negative matrix factorization and its application in unsupervised point anomaly detection.** JMLR, 2021. [paper](https://dl.acm.org/doi/abs/10.5555/3546258.3546292) 3613 | 3614 | *Imtiaz Ahmed, Xia Ben Hu, Mithun P. Acharya, and Yu Ding.* 3615 | 3616 | 1. **Bayesian nonparametric submodular video partition for robust anomaly detection.** CVPR, 2022. [paper](https://openaccess.thecvf.com/content/CVPR2022/html/Sapkota_Bayesian_Nonparametric_Submodular_Video_Partition_for_Robust_Anomaly_Detection_CVPR_2022_paper.html) 3617 | 3618 | *Hitesh Sapkota and Qi Yu.* 3619 | 3620 | 1. **Making parametric anomaly detection on tabular data non-parametric again.** arXiv, 2024. [paper](https://arxiv.org/abs/2401.17052) 3621 | 3622 | *Hugo Thimonier, Fabrice Popineau, Arpad Rimmel, and Bich-Liên Doan.* 3623 | 3624 | ### [Federated Learning](#content) 3625 | 1. **FADngs: Federated learning for anomaly detection.** TNNLS, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10409269) 3626 | 3627 | *Boyu Dong, Dong Chen, Yu Wu, Siliang Tang, and Yueting Zhuang.* 3628 | 3629 | 1. **PeFAD: A parameter-efficient federated framework for time series anomaly detection.** SIGKDD, 2024. [paper](https://arxiv.org/abs/2406.02318) 3630 | 3631 | *Ronghui Xu, Hao Miao, Senzhang Wang, Philip S. Yu, and Jianxin Wang.* 3632 | 3633 | 1. **Weakly Supervised anomaly detection with privacy preservation under a bi-Level federated learning framework.** ESA, 2024. [paper](https://www.sciencedirect.com/science/article/abs/pii/S0957417424013162) 3634 | 3635 | *Wei Guo and Pingyu Jiang.* 3636 | 3637 | 1. **Federated graph anomaly detection via contrastive self-supervised learning.** TNNLS, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10566052) 3638 | 3639 | *Xiangjie Kong, Wenyi Zhang, Hui Wang, Mingliang Hou, Xin Chen, Xiaoran Yan, and Sajal K. Das.* 3640 | 3641 | 1. **Towards effective federated graph anomaly detection via self-boosted knowledge distillation.** MM, 2024. [paper](https://openreview.net/forum?id=Mj1yycahqi) 3642 | 3643 | *Jinyu Cai, Yunhe Zhang, Zhoumin Lu, Wenzhong Guo, and See-Kiong Ng.* 3644 | 3645 | ### [Onlie Learning](#content) 3646 | 1. **Online adaptive anomaly thresholding with confidence sequences.** ICML, 2024. [paper](https://openreview.net/forum?id=hbsKxUEreL) 3647 | 3648 | *Sophia Huiwen Sun, Abishek Sankararaman, and Balakrishnan Murali Narayanaswamy.* 3649 | 3650 | 3651 | ## [Application](#content) 3652 | ### [Finance](#content) 3653 | 1. **Antibenford subgraphs: Unsupervised anomaly detection in financial networks.** KDD, 2022. [paper](https://dl.acm.org/doi/abs/10.1145/3534678.3539100) 3654 | 3655 | *Tianyi Chen and E. Tsourakakis.* 3656 | 3657 | 1. **Adversarial machine learning attacks against video anomaly detection systems.** CVPR, 2022. [paper](https://openaccess.thecvf.com/content/CVPR2022W/ArtOfRobust/html/Mumcu_Adversarial_Machine_Learning_Attacks_Against_Video_Anomaly_Detection_Systems_CVPRW_2022_paper.html) 3658 | 3659 | *Furkan Mumcu, Keval Doshi, and Yasin Yilmaz.* 3660 | 3661 | 1. **Financial time series forecasting using CNN and Transformer.** AAAI, 2023. [paper](https://arxiv.org/abs/2304.04912) 3662 | 3663 | *Zhen Zeng, Rachneet Kaur, Suchetha Siddagangappa, Saba Rahimi, Tucker Balch, and Manuela Veloso.* 3664 | 3665 | 1. **WAKE: A weakly supervised business process anomaly detection framework via a pre-trained autoencoder.** TKDE, 2023. [paper](https://ieeexplore.ieee.org/abstract/document/10285076) 3666 | 3667 | *Wei Guan, Jian Cao, Haiyan Zhao, Yang Gu, and Shiyou Qian.* 3668 | 3669 | 1. **Probabilistic sampling-enhanced temporalspatial GCN: A scalable framework for transaction anomaly detection in Ethereum networks.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.00144) 3670 | 3671 | *Stefan Kambiz Behfar and Jon Crowcroft.* 3672 | 3673 | 1. **Making the end-user a priority in benchmarking: OrionBench for unsupervised time series anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2310.17748) 3674 | 3675 | *Sarah Alnegheimish, Laure Berti-Equille, and Kalyan Veeramachaneni.* 3676 | 3677 | 1. **Graph autoencoder anomaly detection for e-commerce application by contextual integrating contrast with reconstruction and complementarity.** TCE, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10388006) 3678 | 3679 | *Jinjie Wu, Zhipeng Qiu, Zhixia Zeng, Ruliang Xiao, Imad Rida, and Shi Zhang.* 3680 | 3681 | 1. **Unsupervised anomaly detection on attributed networks with graph contrastive learning for consumer electronics security.** TCE, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10402014) 3682 | 3683 | *Bo Xu, Jinpeng Wang, Zhehuan Zhao, Hongfei Lin, and Feng Xia.* 3684 | 3685 | 1. **Fin-Fed-OD: Federated outlier detection on financial tabular data.** arXiv, 2024. [paper](https://arxiv.org/abs/2404.14933) 3686 | 3687 | *Dayananda Herurkar, Sebastian Palacio, Ahmed Anwar, Joern Hees, and Andreas Dengel.* 3688 | 3689 | 1. **Advancing anomaly detection: Non-semantic financial data encoding with LLMs.** arXiv, 2024. [paper](https://arxiv.org/abs/2406.03614) 3690 | 3691 | *Alexander Bakumenko, Kateřina Hlaváčková-Schindler, Claudia Plant, and Nina C. Hubig.* 3692 | 3693 | 1. **Automated financial time series anomaly detection via curiosity-guided exploration and self-imitation learning.** EAAI, 2024. [paper](https://www.sciencedirect.com/science/article/abs/pii/S0952197624008212) 3694 | 3695 | *Feifei Cao and Xitong Guo.* 3696 | 3697 | 1. **Survey and benchmark of anomaly detection in business processes.** TKDE, 2024. [paper](https://ieeexplore.ieee.org/abstract/document/10738505) 3698 | 3699 | *Wei Guan, Jian Cao, Haiyan Zhao, Yang Gu, and Shiyou Qian.* 3700 | 3701 | ### [Point Cloud](#content) 3702 | 1. **Teacher-student network for 3D point cloud anomaly detection with few normal samples.** arXiv, 2022. [paper](https://arxiv.org/abs/2210.17258) 3703 | 3704 | *Jianjian Qin, Chunzhi Gu, Jun Yu, and Chao Zhang.* 3705 | 3706 | 1. **Teacher-student network for 3D point cloud anomaly detection with few normal samples.** WACV, 2023. [paper](https://openaccess.thecvf.com/content/WACV2023/html/Bergmann_Anomaly_Detection_in_3D_Point_Clouds_Using_Deep_Geometric_Descriptors_WACV_2023_paper.html) 3707 | 3708 | *Paul Bergmann and David Sattlegger.* 3709 | 3710 | 1. **Anomaly detection in 3D point clouds using deep geometric descriptors.** WACV, 2023. [paper](https://openaccess.thecvf.com/content/WACV2023/html/Bergmann_Anomaly_Detection_in_3D_Point_Clouds_Using_Deep_Geometric_Descriptors_WACV_2023_paper.html) 3711 | 3712 | *Lokesh Veeramacheneni and Matias Valdenegro-Toro.* 3713 | 3714 | 1. **Learning point-wise abstaining penalty for point cloud anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2309.10230) 3715 | 3716 | *Shaocong Xu, Pengfei Li, Xinyu Liu, Qianpu Sun, Yang Li, Shihui Guo, Zhen Wang, Bo Jiang, Rui Wang, Kehua Sheng, Bo Zhang, and Hao Zhao.* 3717 | 3718 | 1. **Real3D-AD: A dataset of point cloud anomaly detection.** arXiv, 2023. [paper](https://arxiv.org/abs/2309.13226) 3719 | 3720 | *Jiaqi Liu, Guoyang Xie, Ruitao Chen, Xinpeng Li, Jinbao Wang, Yong Liu, Chengjie Wang, and Feng Zheng.* 3721 | 3722 | 1. **Cheating depth: Enhancing 3D surface anomaly detection via depth simulation.** WACV, 2024. [paper](https://arxiv.org/abs/2311.01117) 3723 | 3724 | *Vitjan Zavrtanik, Matej Kristan, and Danijel Skocaj.* 3725 | 3726 | 1. **Image-pointcloud fusion based anomaly detection using PD-REAL dataset.** arXiv, 2023. [paper](https://arxiv.org/abs/2311.04095) 3727 | 3728 | *Jianjian Qin, Chunzhi Gu, Jun Yu, and Chao Zhang.* 3729 | 3730 | 1. **Back to the feature: Classical 3D features are (almost) all you need for 3D anomaly detection.** CVPR, 2023. [paper](https://openaccess.thecvf.com/content/CVPR2023W/VAND/html/Horwitz_Back_to_the_Feature_Classical_3D_Features_Are_Almost_All_CVPRW_2023_paper.html) 3731 | 3732 | *Eliahu Horwitz and Yedid Hoshen.* 3733 | 3734 | 1. **SplatPose & detect: Pose-agnostic 3D anomaly detection.** CVPR, 2024. [paper](https://arxiv.org/abs/2404.06832) 3735 | 3736 | *Mathis Kruse, Marco Rudolph, Dominik Woiwode, and Bodo Rosenhahn.* 3737 | 3738 | 1. **Looking 3D: Anomaly detection with 2D-3D alignment.** CVPR, 2024. [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Bhunia_Looking_3D_Anomaly_Detection_with_2D-3D_Alignment_CVPR_2024_paper.html) 3739 | 3740 | *Ankan Bhunia, Changjian Li, and Hakan Bilen.* 3741 | 3742 | 1. **PointAD: Comprehending 3D anomalies from points and pixels for zero-shot 3D anomaly detection.** NIPS, 2024. [paper](https://arxiv.org/abs/2410.00320) 3743 | 3744 | *Qihang Zhou, Jiangtao Yan, Shibo He, Wenchao Meng, and Jiming Chen.* 3745 | 3746 | 1. **PO3AD: Predicting point offsets toward better 3D point cloud anomaly detection.** CVPR, 2025. [paper](https://arxiv.org/abs/2412.12617) 3747 | 3748 | *Jianan Ye, Weiguang Zhao, Xi Yang, Guangliang Cheng, and Kaizhu Huang.* 3749 | 3750 | ### [Autonomous Driving](#content) 3751 | 1. **DeepSegmenter: Temporal action localization for detecting anomalies in untrimmed naturalistic driving videos.** arXiv, 2023. [paper](https://arxiv.org/abs/2304.08261) 3752 | 3753 | *Armstrong Aboah, Ulas Bagci, Abdul Rashid Mussah, Neema Jakisa Owor, and Yaw Adu-Gyamfi.* 3754 | 3755 | 1. **Multivariate time-series anomaly detection with temporal self-supervision and graphs: Application to vehicle failure prediction.** ECML PKDD, 2023. [paper](https://link.springer.com/chapter/10.1007/978-3-031-43430-3_15) 3756 | 3757 | *Hadi Hojjati, Mohammadreza Sadeghi, and Narges Armanfard.* 3758 | 3759 | 1. **Traffic anomaly detection: Exploiting temporal positioning of flow-density samples.** TITS, 2023. [paper](https://ieeexplore.ieee.org/abstract/document/10287217) 3760 | 3761 | *Iman Taheri Sarteshnizi, Saeed Asadi Bagloee, Majid Sarvi, and Neema Nassir.* 3762 | 3763 | 1. **An expert ensemble for detecting anomalous scenes, interactions, and behaviors in autonomous driving.** IJRR, 2025. [paper](https://ieeexplore.ieee.org/abstract/document/10287217) 3764 | 3765 | *Tianchen Ji, Neeloy Chakraborty, Andre Schreiber, and Katherine Driggs-Campbell.* 3766 | 3767 | ### [Medical Image](#content) 3768 | 1. **SWSSL: Sliding window-based self-supervised learning for anomaly detection in high-resolution images.** TMI, 2023. [paper](https://ieeexplore.ieee.org/abstract/document/10247020) 3769 | 3770 | *Haoyu Dong, Yifan Zhang, Hanxue Gu, Nicholas Konz, Yixin Zhang, and Maciej A Mazurowskii.* 3771 | 3772 | 1. **A model-agnostic framework for universal anomaly detection of multi-organ and multi-modal images.** MICCAI, 2023. [paper](https://link.springer.com/chapter/10.1007/978-3-031-43898-1_23) 3773 | 3774 | *Yinghao Zhang, Donghuan Lu, Munan Ning, Liansheng Wang, Dong Wei, and Yefeng Zheng.* 3775 | 3776 | 1. **Dual conditioned diffusion models for out-of-distribution detection: Application to fetal ultrasound videos.** MICCAI, 2023. [paper](https://link.springer.com/chapter/10.1007/978-3-031-43907-0_21) 3777 | 3778 | *Divyanshu Mishra, He Zhao, Pramit Saha, Aris T. Papageorghiou, and J. Alison Noble.* 3779 | 3780 | 1. **MAEDiff: Masked autoencoder-enhanced diffusion models for unsupervised anomaly detection in brain images.** arXiv, 2024. [paper](https://arxiv.org/abs/2401.10561) 3781 | 3782 | *Rui Xu, Yunke Wang, and Bo Du.* 3783 | 3784 | 1. **Domain adaptive and fine-grained anomaly detection for single-cell sequencing data and beyond.** IJCAI, 2024. [paper](https://arxiv.org/abs/2404.17454) 3785 | 3786 | *Kaichen Xu, Yueyang Ding, Suyang Hou, Weiqiang Zhan, Nisang Chen, Jun Wang, and Xiaobo Sun.* 3787 | 3788 | 1. **Position-guided prompt learning for anomaly detection in chest X-rays.** MICCAI, 2024. [paper](https://arxiv.org/abs/2405.11976) 3789 | 3790 | *Zhichao Sun, Yuliang Gu, Yepeng Liu, Zerui Zhang, Zhou Zhao, and Yongchao Xu.* 3791 | 3792 | 1. **MediCLIP: Adapting CLIP for few-shot medical image anomaly detection.** MICCAI, 2024. [paper](https://arxiv.org/abs/2405.11315) 3793 | 3794 | *Ximiao Zhang, Min Xu, Dehui Qiu, Ruixin Yan, Ning Lang, and Xiuzhuang Zhou.* 3795 | 3796 | 1. **Spatial-aware attention generative adversarial network for semi-supervised anomaly detection in medical image.** MICCAI, 2024. [paper](https://arxiv.org/abs/2405.12872) 3797 | 3798 | *Zerui Zhang, Zhichao Sun, Zelong Liu, Bo Du, Rui Yu, Zhou Zhao, and Yongchao Xu.* 3799 | 3800 | 1. **Leveraging the mahalanobis distance to enhance unsupervised brain MRI anomaly detection.** MICCAI, 2024. 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[paper](https://arxiv.org/abs/2310.07585) 3916 | 3917 | *Yuxuan Cai, Dingkang Liang, Dongliang Luo, Xinwei He, Xin Yang, and Xiang Bai.* 3918 | 3919 | 1. **Anomaly detection with memory-augmented adversarial autoencoder networks for industry 5.0.** TCE, 2023. [paper](https://ieeexplore.ieee.org/abstract/document/10263623) 3920 | 3921 | *Huan Zhang, Neeraj Kumar, Sheng Wu, Chunlei Wu, Jian Wang, and Peiying Zhang.* 3922 | 3923 | 1. **Towards total online unsupervised anomaly detection and localization in industrial vision.** arXiv, 2023. [paper](https://arxiv.org/abs/2305.15652) 3924 | 3925 | *Han Gao, Huiyuan Luo, Fei Shen, and Zhengtao Zhang.* 3926 | 3927 | 1. **Self-supervised variational graph autoencoder for system-level anomaly detection.** TIM, 2023. [paper](https://ieeexplore.ieee.org/abstract/document/10285620) 3928 | 3929 | *Le Zhang, Wei Cheng, Ji Xing, Xuefeng Chen, Zelin Nie, Shuo Zhang, Junying Hong, and Zhao Xu.* 3930 | 3931 | 1. **Distillation-based fabric anomaly detection.** arXiv, 2024. [paper](https://arxiv.org/abs/2401.02287) 3932 | 3933 | *Simon Thomine and Hichem Snoussi.* 3934 | 3935 | 1. **Towards total online unsupervised anomaly detection and localization in industrial vision.** arXiv, 2024. [paper](https://arxiv.org/abs/2305.15652) 3936 | 3937 | *Han Gao, Huiyuan Luo, Fei Shen, and Zhengtao Zhang.* 3938 | 3939 | 1. **Adaptable and interpretable framework for anomaly detection in SCADA-based industrial systems.** ESA, 2024. [paper](https://www.sciencedirect.com/science/article/pii/S0957417424000654) 3940 | 3941 | *Marek Wadinger and Michal Kvasnica.* 3942 | 3943 | 1. **Graph structure change-based anomaly detection in multivariate time series of industrial processes.** TII, 2024. 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[paper](https://arxiv.org/abs/2408.04254) 4020 | 4021 | *Dongqi Fu, Yada Zhu, Hanghang Tong, Kommy Weldemariam, Onkar Bhardwaj, and Jingrui He* 4022 | --------------------------------------------------------------------------------