└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # AdversarialNetsPapers 2 | The classical Papers about adversarial nets 3 | 4 | The First paper 5 | -------------------------------------------- 6 | :white_check_mark: [Generative Adversarial Nets] [[Paper]](https://arxiv.org/abs/1406.2661) 7 | [[Code]](https://github.com/goodfeli/adversarial)(the first paper about it) 8 | 9 | ## Unclassified 10 | 11 | :white_check_mark: [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks] [[Paper]](https://arxiv.org/abs/1506.05751)[[Code]](https://github.com/facebook/eyescream) 12 | 13 | :white_check_mark: [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1511.06434)[[Code]](https://github.com/jacobgil/keras-dcgan)(Gan with convolutional networks)(ICLR) 14 | 15 | :white_check_mark: [Adversarial Autoencoders] [[Paper]](http://arxiv.org/abs/1511.05644)[[Code]](https://github.com/musyoku/adversarial-autoencoder) 16 | 17 | 18 | :white_check_mark: [Generating Images with Perceptual Similarity Metrics based on Deep Networks] [[Paper]](https://arxiv.org/pdf/1602.02644v2.pdf) 19 | 20 | 21 | :white_check_mark: [Generating images with recurrent adversarial networks] [[Paper]](https://arxiv.org/abs/1602.05110)[[Code]](https://github.com/ofirnachum/sequence_gan) 22 | 23 | :white_check_mark: [Generative Visual Manipulation on the Natural Image Manifold] [[Paper]](https://people.eecs.berkeley.edu/~junyanz/projects/gvm/eccv16_gvm.pdf)[[Code]](https://github.com/junyanz/iGAN) 24 | 25 | :white_check_mark: [Generative Adversarial Text to Image Synthesis] [[Paper]](https://arxiv.org/abs/1605.05396)[[Code]](https://github.com/reedscot/icml2016)[[code]](https://github.com/paarthneekhara/text-to-image) 26 | 27 | 28 | :white_check_mark: [Learning What and Where to Draw] [[Paper]](http://www.scottreed.info/files/nips2016.pdf)[[Code]](https://github.com/reedscot/nips2016) 29 | 30 | :white_check_mark: [Adversarial Training for Sketch Retrieval] [[Paper]](http://link.springer.com/chapter/10.1007/978-3-319-46604-0_55) 31 | 32 | :white_check_mark: [Generative Image Modeling using Style and Structure Adversarial Networks] [[Paper]](https://arxiv.org/pdf/1603.05631.pdf)[[Code]](https://github.com/xiaolonw/ss-gan) 33 | 34 | :white_check_mark: [Generative Adversarial Networks as Variational Training of Energy Based Models] [[Paper]](http://www.mathpubs.com/detail/1611.01799v1/Generative-Adversarial-Networks-as-Variational-Training-of-Energy-Based-Models)(ICLR 2017) 35 | 36 | :white_check_mark: [Adversarial Training Methods for Semi-Supervised Text Classification] [[Paper]](https://arxiv.org/abs/1605.07725)[[Note]](https://github.com/dennybritz/deeplearning-papernotes/blob/master/notes/adversarial-text-classification.md)( Ian Goodfellow Paper) 37 | 38 | :white_check_mark: [Learning from Simulated and Unsupervised Images through Adversarial Training] [[Paper]](https://arxiv.org/abs/1612.07828)[[code]](https://github.com/carpedm20/simulated-unsupervised-tensorflow)（Apple paper） 39 | 40 | :white_check_mark: [Synthesizing the preferred inputs for neurons in neural networks via deep generator networks] [[Paper]](https://arxiv.org/pdf/1605.09304v5.pdf)[[Code]](https://github.com/Evolving-AI-Lab/synthesizing) 41 | 42 | :white_check_mark: [SalGAN: Visual Saliency Prediction with Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1701.01081)[[Code]](https://github.com/imatge-upc/saliency-salgan-2017) 43 | 44 | :white_check_mark: [Adversarial Feature Learning] [[Paper]](https://arxiv.org/abs/1605.09782) 45 | 46 | :white_check_mark: [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks] [[Paper]](https://junyanz.github.io/CycleGAN/)[[Code]](https://github.com/junyanz/CycleGAN) 47 | 48 | ## Ensemble 49 | 50 | :white_check_mark: [AdaGAN: Boosting Generative Models] [[Paper]](https://arxiv.org/abs/1701.02386)[[Code]]（Google Brain） 51 | 52 | ## Clustering 53 | 54 | :white_check_mark: [Unsupervised Learning Using Generative Adversarial Training And Clustering] [[Paper]](https://openreview.net/forum?id=SJ8BZTjeg¬eId=SJ8BZTjeg)[[Code]](https://github.com/VittalP/UnsupGAN)(ICLR) 55 | :white_check_mark: [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1511.06390)(ICLR) 56 | 57 | ## Image Inpainting 58 | 59 | :white_check_mark: [Semantic Image Inpainting with Perceptual and Contextual Losses] [[Paper]](https://arxiv.org/abs/1607.07539)[[Code]](https://github.com/bamos/dcgan-completion.tensorflow) 60 | 61 | :white_check_mark: [Context Encoders: Feature Learning by Inpainting] [[Paper]](https://arxiv.org/abs/1604.07379)[[Code]](https://github.com/jazzsaxmafia/Inpainting) 62 | 63 | :white_check_mark: [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1611.06430v1) 64 | 65 | ## Joint Probability 66 | 67 | :white_check_mark: [Adversarially Learned Inference][[Paper]](https://arxiv.org/abs/1606.00704)[[Code]](https://github.com/IshmaelBelghazi/ALI) 68 | 69 | ## Super-Resolution 70 | 71 | :white_check_mark: [Image super-resolution through deep learning ][[Code]](https://github.com/david-gpu/srez)(Just for face dataset) 72 | 73 | :white_check_mark: [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network] [[Paper]](https://arxiv.org/abs/1609.04802)[[Code]](https://github.com/leehomyc/Photo-Realistic-Super-Resoluton)（Using Deep residual network） 74 | 75 | :white_check_mark: [EnhanceGAN] [[Docs]](https://medium.com/@richardherbert/faces-from-noise-super-enhancing-8x8-images-with-enhancegan-ebda015bb5e0#.io6pskvin)[[Code]] 76 | 77 | 78 | ## Disocclusion 79 | 80 | :white_check_mark: [Robust LSTM-Autoencoders for Face De-Occlusion in the Wild] [[Paper]](https://arxiv.org/abs/1612.08534) 81 | 82 | ## Semantic Segmentation 83 | 84 | :white_check_mark: [Semantic Segmentation using Adversarial Networks] [[Paper]](https://arxiv.org/abs/1611.08408)（soumith's paper） 85 | 86 | ## Object Detection 87 | 88 | :white_check_mark: [Perceptual generative adversarial networks for small object detection] [[Paper]]（Submitted） 89 | 90 | :white_check_mark: [A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection] [[Paper]](http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf)(CVPR2017) 91 | 92 | ## RNN 93 | 94 | :white_check_mark: [C-RNN-GAN: Continuous recurrent neural networks with adversarial training] [[Paper]](https://arxiv.org/abs/1611.09904)[[Code]](https://github.com/olofmogren/c-rnn-gan) 95 | 96 | ## Conditional adversarial 97 | 98 | :white_check_mark: [Conditional Generative Adversarial Nets] [[Paper]](https://arxiv.org/abs/1411.1784)[[Code]](https://github.com/zhangqianhui/Conditional-Gans) 99 | 100 | :white_check_mark: [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets] [[Paper]](https://arxiv.org/abs/1606.03657)[[Code]](https://github.com/buriburisuri/supervised_infogan) 101 | 102 | :white_check_mark: [Image-to-image translation using conditional adversarial nets] [[Paper]](https://arxiv.org/pdf/1611.07004v1.pdf)[[Code]](https://github.com/phillipi/pix2pix)[[Code]](https://github.com/yenchenlin/pix2pix-tensorflow) 103 | 104 | :white_check_mark: [Conditional Image Synthesis With Auxiliary Classifier GANs] [[Paper]](https://arxiv.org/abs/1610.09585)[[Code]](https://github.com/buriburisuri/ac-gan)(GoogleBrain ICLR 2017) 105 | 106 | :white_check_mark: [Pixel-Level Domain Transfer] [[Paper]](https://arxiv.org/pdf/1603.07442v2.pdf)[[Code]](https://github.com/fxia22/pldtgan) 107 | 108 | :white_check_mark: [Invertible Conditional GANs for image editing] [[Paper]](https://arxiv.org/abs/1611.06355)[[Code]](https://github.com/Guim3/IcGAN) 109 | 110 | :white_check_mark: [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space] [[Paper]](https://arxiv.org/abs/1612.00005v1)[[Code]](https://github.com/Evolving-AI-Lab/ppgn) 111 | 112 | :white_check_mark: [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks] [[Paper]](https://arxiv.org/pdf/1612.03242v1.pdf)[[Code]](https://github.com/hanzhanggit/StackGAN) 113 | 114 | :white_check_mark: [Unsupervised Image-to-Image Translation with Generative Adversarial Networks] [[Paper]](https://arxiv.org/pdf/1701.02676.pdf) 115 | 116 | :white_check_mark: [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1703.05192)[[Code]](https://github.com/carpedm20/DiscoGAN-pytorch) 117 | 118 | ## Video Prediction 119 | 120 | :white_check_mark: [Deep multi-scale video prediction beyond mean square error] [[Paper]](https://arxiv.org/abs/1511.05440)[[Code]](https://github.com/dyelax/Adversarial_Video_Generation)(Yann LeCun's paper) 121 | 122 | :white_check_mark: [Unsupervised Learning for Physical Interaction through Video Prediction] [[Paper]](https://arxiv.org/abs/1605.07157)(Ian Goodfellow's paper) 123 | 124 | :white_check_mark: [Generating Videos with Scene Dynamics] [[Paper]](https://arxiv.org/abs/1609.02612)[[Web]](http://web.mit.edu/vondrick/tinyvideo/)[[Code]](https://github.com/cvondrick/videogan) 125 | 126 | ## Texture Synthesis & style transfer 127 | 128 | :white_check_mark: [Precomputed real-time texture synthesis with markovian generative adversarial networks] [[Paper]](https://arxiv.org/abs/1604.04382)[[Code]](https://github.com/chuanli11/MGANs)(ECCV 2016) 129 | 130 | 131 | ## GAN Theory 132 | 133 | :white_check_mark: [Energy-based generative adversarial network] [[Paper]](https://arxiv.org/pdf/1609.03126v2.pdf)[[Code]](https://github.com/buriburisuri/ebgan)(Lecun paper) 134 | 135 | :white_check_mark: [Improved Techniques for Training GANs] [[Paper]](https://arxiv.org/abs/1606.03498)[[Code]](https://github.com/openai/improved-gan)(Goodfellow's paper) 136 | 137 | :white_check_mark: [Mode RegularizedGenerative Adversarial Networks] [[Paper]](https://openreview.net/pdf?id=HJKkY35le)(Yoshua Bengio , ICLR 2017) 138 | 139 | :white_check_mark: [Improving Generative Adversarial Networks with Denoising Feature Matching] [[Paper]](https://openreview.net/pdf?id=S1X7nhsxl)[[Code]](https://github.com/hvy/chainer-gan-denoising-feature-matching)(Yoshua Bengio , ICLR 2017) 140 | 141 | :white_check_mark: [Sampling Generative Networks] [[Paper]](https://arxiv.org/abs/1609.04468)[[Code]](https://github.com/dribnet/plat) 142 | 143 | :white_check_mark: [Mode Regularized Generative Adversarial Networkss] [[Paper]](https://arxiv.org/abs/1612.02136)( Yoshua Bengio's paper) 144 | 145 | :white_check_mark: [How to train Gans] [[Docu]](https://github.com/soumith/ganhacks#authors) 146 | 147 | :white_check_mark: [Towards Principled Methods for Training Generative Adversarial Networks] [[Paper]](http://openreview.net/forum?id=Hk4_qw5xe)(ICLR 2017) 148 | 149 | :white_check_mark: [Unrolled Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1611.02163)[[Code]](https://github.com/poolio/unrolled_gan) 150 | 151 | :white_check_mark: [Least Squares Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1611.04076)[[Code]](https://github.com/pfnet-research/chainer-LSGAN) 152 | 153 | :white_check_mark: [Wasserstein GAN] [[Paper]](https://arxiv.org/abs/1701.07875)[[Code]](https://github.com/martinarjovsky/WassersteinGAN) 154 | 155 | :white_check_mark: [Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities] [[Paper]](https://arxiv.org/abs/1701.06264)[[Code]](https://github.com/guojunq/lsgan)(The same as WGan) 156 | 157 | :white_check_mark: [Towards Principled Methods for Training Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1701.04862) 158 | 159 | 160 | ## 3D 161 | 162 | :white_check_mark: [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling] [[Paper]](https://arxiv.org/abs/1610.07584)[[Web]](http://3dgan.csail.mit.edu/)[[code]](https://github.com/zck119/3dgan-release)(2016 NIPS) 163 | 164 | ## Face Generative and Editing 165 | 166 | :white_check_mark: [Autoencoding beyond pixels using a learned similarity metric] [[Paper]](https://arxiv.org/abs/1512.09300)[[code]](https://github.com/andersbll/autoencoding_beyond_pixels) 167 | 168 | :white_check_mark: [Coupled Generative Adversarial Networks] [[Paper]](http://mingyuliu.net/)[[Caffe Code]](https://github.com/mingyuliutw/CoGAN)[[Tensorflow Code]](https://github.com/andrewliao11/CoGAN-tensorflow)（NIPS） 169 | 170 | :white_check_mark: [Invertible Conditional GANs for image editing] [[Paper]](https://drive.google.com/file/d/0B48XS5sLi1OlRkRIbkZWUmdoQmM/view)[[Code]](https://github.com/Guim3/IcGAN) 171 | 172 | :white_check_mark: [Learning Residual Images for Face Attribute Manipulation] [[Paper]](https://arxiv.org/abs/1612.05363) 173 | 174 | :white_check_mark: [Neural Photo Editing with Introspective Adversarial Networks] [[Paper]](https://arxiv.org/abs/1609.07093)[[Code]](https://github.com/ajbrock/Neural-Photo-Editor)(ICLR 2017) 175 | 176 | ## For discrete distributions 177 | 178 | :white_check_mark: [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1702.07983v1) 179 | 180 | :white_check_mark: [Boundary-Seeking Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1702.08431) 181 | 182 | :white_check_mark: [GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution] [[Paper]](https://arxiv.org/abs/1611.04051) 183 | 184 | # Project 185 | 186 | :white_check_mark: [cleverhans] [[Code]](https://github.com/openai/cleverhans)(A library for benchmarking vulnerability to adversarial examples) 187 | 188 | :white_check_mark: [reset-cppn-gan-tensorflow] [[Code]](https://github.com/hardmaru/resnet-cppn-gan-tensorflow)(Using Residual Generative Adversarial Networks and Variational Auto-encoder techniques to produce high resolution images) 189 | 190 | :white_check_mark: [HyperGAN] [[Code]](https://github.com/255bits/HyperGAN)(Open source GAN focused on scale and usability) 191 | 192 | # Blogs 193 | | Author | Address | 194 | |:----:|:---:| 195 | | **inFERENCe** | [Adversarial network](http://www.inference.vc/) | 196 | | **inFERENCe** | [InfoGan](http://www.inference.vc/infogan-variational-bound-on-mutual-information-twice/) | 197 | | **distill** | [Deconvolution and Image Generation](http://distill.pub/2016/deconv-checkerboard/) | 198 | | **yingzhenli** | [Gan theory](http://www.yingzhenli.net/home/blog/?p=421http://www.yingzhenli.net/home/blog/?p=421) | 199 | | **OpenAI** | [Generative model](https://openai.com/blog/generative-models/) | 200 | 201 | 202 | # Other 203 | 204 | :white_check_mark: [1] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[[Chinese Trans]](http://c.m.163.com/news/a/C7UE2MLT0511AQHO.html?spss=newsapp&spsw=1)[[details]](https://arxiv.org/pdf/1701.00160v1.pdf) 205 | 206 | :white_check_mark: [2] [[PDF]](https://drive.google.com/file/d/0BxKBnD5y2M8NbzBUbXRwUDBZOVU/view)(NIPS Lecun Slides) 207 | 208 | 209 | 210 | --------------------------------------------------------------------------------