├── README.md
└── assets
├── approaches
├── Adaptation-Aware.svg
├── Contrastive Learning.svg
├── Dynamic Network Architecture.svg
├── Ensemble Large Pretrained Vision Models.svg
├── Feature Enhancement.svg
├── Feature-level Augmentation.svg
├── Fusion.svg
├── Image-level Augmentation.svg
├── Knowledge Distillation.svg
├── Latent Space.svg
├── Masking.svg
├── Modulation.svg
├── Natural-Language Guided.svg
├── Non-Progressive Training.svg
├── Optimization.svg
├── Other Multi-Task Objectives.svg
├── Progressive Training.svg
├── Prompt Tuning.svg
├── Prototype Learning.svg
├── Regularizer-based Fine-Tuning.svg
├── Regularizer.svg
├── Transformation-Driven Design.svg
└── Transformation.svg
├── model
├── DM.svg
├── GAN.svg
├── VAE.svg
└── VQ-VAE.svg
└── task
├── IGM.svg
├── SGM.svg
├── cGM-1.svg
├── cGM-2.svg
├── cGM-3.svg
├── uGM-1.svg
├── uGM-2.svg
└── uGM-3.svg
/README.md:
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1 |
2 | [](https://github.com/sindresorhus/awesome)
3 | [](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity)
4 | [](http://makeapullrequest.com)
5 |
6 |
7 | # A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot
8 | ### [Project Page](https://gmdc-survey.github.io) | [Paper](https://arxiv.org/abs/2307.14397) | [Bibtex](#bibtex)
9 | [Milad Abdollahzadeh](https://miladabd.github.io/), [Touba Malekzadeh](https://scholar.google.com/citations?user=DgnZKiQAAAAJ&hl=en)\*, [Christopher T. H. Teo](https://scholar.google.com/citations?user=JhyGETcAAAAJ&hl=en)\*, [Keshigeyan Chandrasegaran](https://keshik6.github.io/)\*, [Guimeng Liu](https://scholar.google.com/citations?user=wJskd84AAAAJ&hl=en&oi=sra), [Ngai-Man Cheung](https://sites.google.com/site/mancheung0407/)†
10 |
11 | (* Equal contribution, † Corresponding author)
12 |
13 |
14 | This repo contains the list of papers with public code implementations for Generative Modeling under Data Constraint (GM-DC).
15 | For each work, we determine the generative task(s) addressed, the approach, and the type of generative model used.
16 |
17 | First, we define the generative tasks, and the approach definition, and then provide our comprehensive list of the works for GM-DC with the required details for each work.
18 |
19 |
20 | ## :star: Overview
21 | > In machine learning, generative modeling aims to learn to generate new data statistically similar to the
22 | training data distribution. In this paper, we survey learning generative models under limited data, few shots
23 | and zero shot, referred to as Generative Modeling under Data Constraint (GM-DC). This is an important
24 | topic when data acquisition is challenging, e.g. healthcare applications. We discuss background, challenges,
25 | and propose two taxonomies: one on GM-DC tasks and another on GM-DC approaches. Importantly, we
26 | study interactions between different GM-DC tasks and approaches. Furthermore, we highlight research gaps,
27 | research trends, and potential avenues for future exploration.
28 |
29 | ## :earth_asia: News
30 | * **Oct 28, 2024:** The slides for our ICIP tutorial on *"Generative Modeling for Limited Data, Few Shots and Zero Shot"* can be found [here](https://drive.google.com/file/d/1L4k2VTywZDnIhl51Or4I-kOcMt26CSP4/view?usp=sharing).
31 | * **July 28, 2023:** First release (113 works included)!
32 |
33 |
34 | ## Generative Tasks Definition
35 | We define 8 different generative tasks under data constraints based on the rigorous review of the literature. The description of these tasks can be found in the follwing table:
36 |
37 | | Task | Description & Example | Illustration |
38 | |:---------|:---------------------:|:------------:|
39 | | **uGM-1** | **Description:** Given $K$ samples from a domain $\mathcal{D}$, learn to generate diverse and high-quality samples from $\mathcal{D}$ **Example:** [ADA](https://arxiv.org/abs/2006.06676) learns a StyleGAN2 using 1k images from AFHQ-Dog| |
40 | | **uGM-2** | **Description:** Given a pre-trained generator on a source domain $\mathcal{D}_s$ and $K$ samples from a target domain $\mathcal{D}_t$, learn to generate diverse and high-quality samples from $\mathcal{D}_t$ **Example:** [CDC](https://arxiv.org/abs/2104.06820) adapts a pre-trained GAN on FFHQ (Human Faces) to Sketches using 10 samples| |
41 | | **uGM-3** | **Description:** Given a pre-trained generator on a source domain $\mathcal{D}_s$ and a text prompt describing a target domain $\mathcal{D}_t$, learn to generate diverse and high-quality samples from $\mathcal{D}_t$ **Example:** [StyleGAN-NADA](https://arxiv.org/abs/2108.00946) adapts pre-trained GAN on FFHQ to the painting domain using `Fernando Botero Painting` as input| |
42 | | **cGM-1** | **Description:** Given $K$ samples with class labels from a domain $\mathcal{D}$, learn to generate diverse and high-quality samples conditioning on the class labels from $\mathcal{D}$ **Example:** [CbC](https://arxiv.org/abs/2201.06578) trains conditional generator on 20 classes of ImageNet Carnivores using 100 images per class | |
43 | | **cGM-2** | **Description:** Given a pre-trained generator on the seen classes $C_{seen}$ of a domain $\mathcal{D}$ and $K$ samples with class labels from unseen classes $C_{unseen}$ of $\mathcal{D}$, learn to generate diverse and high-quality samples conditioning on the class labels for $C_{unseen}$ from $\mathcal{D}$ **Example:** [LoFGAN](https://ieeexplore.ieee.org/document/9710556) learns from 85 classes of Flowers to generate images for an unseen class with only 3 samples| |
44 | | **cGM-3** | **Description:** Given a pre-trained generator on a source domain $\mathcal{D}_s$ and $K$ samples with class labels from a target domain $\mathcal{D}_t$ , learn to generate diverse and high-quality samples conditioning on the class labels from $\mathcal{D}_t$ **Example:** [VPT](https://arxiv.org/abs/2210.00990) adapts a pre-trained conditional generator on ImageNet to Places365 with 500 images per class| |
45 | | **IGM** | **Description:** Given $K$ samples (usually $K=1$) and assuming rich internal distribution for patches within these samples, learn to generate diverse and high-quality samples with the same internal patch distribution **Example:** [SinDDM](https://arxiv.org/abs/2211.16582) trains a generator using a single image of Marina Bay Sands, and generates variants of it ||
46 | | **SGM** | **Description:** Given a pre-trained generator, $K$ samples of a particular subject, and a text prompt, learn to generate diverse and high-quality samples containing the same subject **Example:** [DreamBooth](https://arxiv.org/abs/2208.12242) trains a generator using 4 images of a particular backpack and adapts it with a text-prompt to be in the `grand canyon` ||
47 |
48 | Please refer to our survey for a more detailed discussion of these generative tasks including the attributes of each task and the data limitation range that addressed for each task.
49 |
50 |
51 |
52 |
53 |
54 |
55 |
65 |
66 |
67 |
Transfer Learning
68 |
69 |
70 | Click to expand/collapse 50 works
71 |
72 | - **Transferring GANs: generating images from limited data** ECCV 2018 [Paper] [Official Code] [](#0) [](#0) [](#0) [](#0)
73 | - **Image Generation from Small Datasets via Batch Statistics Adaptation** ICCV 2019 [Paper] [Official Code] [](#2) [](#2) [](#2)
74 | - **Freeze the Discriminator: a Simple Baseline for Fine-tuning GANs** CVPR 2020-W [Paper] [Official Code] [](#3) [](#3) [](#3)
75 | - **On Leveraging Pretrained GANs for Generation with Limited Data** ICML 2020 [Paper] [Official Code] [](#8) [](#8) [](#8)
76 | - **Few-Shot Image Generation with Elastic Weight Consolidation** NeurIPS 2020 [Paper] [](#9) [](#9) [](#9)
77 | - **GAN Memory with No Forgetting** NeurIPS 2020 [Paper] [Official Code] [](#10) [](#10) [](#10)
78 | - **Few-Shot Adaptation of Generative Adversarial Networks** arXiv 2020 [Paper] [Official Code] [](#31) [](#31) [](#31)
79 | - **Effective Knowledge Transfer from GANs to Target domains with Few Images** CVPR 2021 [Paper] [Official Code] [](#4) [](#4) [](#4) [](#4)
80 | - **Few-Shot Image Generation via Cross-domain Correspondence** CVPR 2021 [Paper] [Official Code] [](#12) [](#12) [](#12)
81 | - **Efficient Conditional GAN Transfer with Konwledge Propagation across Classes** CVPR 2021 [Paper] [Official Code] [](#13) [](#13) [](#13) [](#13)
82 | - **CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks** NeurIPS 2021 [Paper] [Official Code] [](#11) [](#11) [](#11)
83 | - **Contrastive Learning for Cross-domain Correspondence in Few-shot Image Generation** NeurIPS 2021-W [Paper] [](#41) [](#41) [](#41)
84 | - **Instance-Conditioned GAN** NeurIPS 2021 [Paper] [Official Code] [](#48) [](#48) [](#48)
85 | - **Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains** arXiv 2021 [Paper] [Official Code] [](#6) [](#6) [](#6) [](#6)
86 | - **One-Shot Generative Domain Adaptation** arXiv 2021 [Paper] [Official Code] [](#30) [](#30) [](#30)
87 | - **When, Why, and Which Pre-trained GANs are useful?** ICLR 2022 [Paper] [Official Code] [](#15) [](#15) [](#15)
88 | - **Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks** ICLR 2022 [Paper] [Official Code] [](#23) [](#23) [](#23)
89 | - **A Closer Look at Few-Shot Image Generation** CVPR 2022 [Paper] [](#16) [](#16) [](#16)
90 | - **Few shot generative model adaption via relaxed spatial structural alignment** CVPR 2022 [Paper] [Official Code] [](#17) [](#17) [](#17)
91 | - **One Shot Face Stylization** ECCV 2022 [Paper] [Official Code] [](#32) [](#32) [](#32)
92 | - **Few-shot Image Generation via Adaptation-Aware Kernel Modulation** NeurIPS 2022 [Paper] [Official Code] [](#18) [](#18) [](#18) [](#18)
93 | - **Universal Domain Adaptation for Generative Adversarial Networks** NeurIPS 2022 [Paper] [Official Code] [](#24) [](#24) [](#24) [](#24) [](#24)
94 | - **Generalized One-shot Domain Adaptation of Generative Adversarial Networks** NeurIPS 2022 [Paper] [Official Code] [](#28) [](#28) [](#28)
95 | - **Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks** NeurIPS 2022 [Paper] [Official Code] [](#58) [](#58) [](#58)
96 | - **CLIP-Guided Domain Adaptation of Image Generators** ACM-TOG 2022 [Paper] [Official Code] [](#22) [](#22) [](#22)
97 | - **Dynamic Few-shot Adaptation of GANs to Multiple Domains** SIGGRAPH-Asia 2022 [Paper] [Official Code] [](#29) [](#29) [](#29)
98 | - **Exploiting Knowledge Distillation for Few-Shot Image Generation** arXiv 2022 [Paper] [](#35) [](#35) [](#35)
99 | - **Few-shot Artistic Portraits Generation with Contrastive Transfer Learning** arXiv 2022 [Paper] [](#36) [](#36) [](#36)
100 | - **Dynamic Weighted Semantic Correspondence for Few-Shot Image Generative Adaptation** ACM-MM 2022 [Paper] [](#50) [](#50) [](#50)
101 | - **Fair Generative Models via Transfer Learning** AAAI 2023 [Paper] [Official Code] [](#20) [](#20) [](#20)
102 | - **Progressive Few-Shot Adaptation of Generative Model with Align-Free Spatial Correlation** AAAI 2023 [Paper] [Official Code] [](#54) [](#54) [](#54)
103 | - **Few-shot Cross-domain Image Generation via Inference-time Latent-code Learning** ICLR 2023 [Paper] [Official Code] [](#37) [](#37) [](#37)
104 | - **Exploring Incompatible Knowledge Transfer in Few-shot Image Generation** CVPR 2023 [Paper] [Official Code] [](#21) [](#21) [](#21)
105 | - **Zero-shot Generative Model Adaptation via Image-specific Prompt Learning** CVPR 2023 [Paper] [Official Code] [](#38) [](#38) [](#38)
106 | - **Visual Prompt Tuning for Generative Transfer Learning** CVPR 2023 [Paper] [Official Code] [](#39) [](#39) [](#39) [](#39)
107 | - **SINgle Image Editing with Text-to-Image Diffusion Models** CVPR 2023 [Paper] [Official Code] [](#42) [](#42) [](#42)
108 | - **DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation** CVPR 2023 [Paper] [](#43) [](#43) [](#43)
109 | - **Multi-Concept Customization of Text-to-Image Diffusion** CVPR 2023 [Paper] [Official Code] [](#44) [](#44) [](#44)
110 | - **Plug-and-Play Sample-Efficient Fine-Tuning of Text-to-Image Diffusion Models to Learn Any Unseen Style** CVPR 2023 [Paper] [](#56) [](#56) [](#56)
111 | - **Target-Aware Generative Augmentations for Single-Shot Adaptation** ICML 2023 [Paper] [Official Code] [](#51) [](#51) [](#51)
112 | - **MultiDiffusion:Fusing Diffusion Paths for Controlled Image Generation** ICML 2023 [Paper] [Official Code] [](#52) [](#52) [](#52)
113 | - **Data-Dependent Domain Transfer GANs for Image Generation with Limited Data** AC-MTMCCA 2023 [Paper] [](#33) [](#33) [](#33)
114 | - **One-Shot Adaptation of GAN in Just One CLIP** TPAMI 2023 [Paper] [Official Code] [](#34) [](#34) [](#34)
115 | - **Few-shot Image Generation via Masked Discrimination** arXiv 2023 [Paper] [](#45) [](#45) [](#45)
116 | - **Few-shot Image Generation via Latent Space Relocation** arXiv 2023 [Paper] [](#46) [](#46) [](#46)
117 | - **Faster Few-Shot Face Image Generation with Features of Specific Group Using Pivotal Tuning Inversion and PCA** ICAIIC 2023 [Paper] [](#47) [](#47) [](#47)
118 | - **Few-shot Image Generation with Diffusion Models** arXiv 2023 [Paper] [](#49) [](#49) [](#49)
119 | - **Rethinking cross-domain semantic relation for few-shot image generation** Applied-Inteligence 2023 [Paper] [Official Code] [](#53) [](#53) [](#53)
120 | - **An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion** arXiv 2023 [Paper] [Official Code] [](#55) [](#55) [](#55)
121 | - **BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing** arXiv 2023 [Paper] [Official Code] [](#57) [](#57) [](#57)
122 |
123 |
124 |
125 |
126 |
127 |
128 | ## Data Augmentation
129 |
130 |
131 | Click to expand/collapse 12 works
132 |
133 | - **Consistency Regularization for Generative Adversarial Networks** ICLR 2019 [Paper] [Official Code] [](#0) [](#0) [](#0)
134 | - **Training generative adversarial networks with limited data** NeurIPS 2020 [Paper] [Official Code] [](#2) [](#2) [](#2) [](#2)
135 | - **Differentiable Augmentation for Data-efficient GAN Training** NeurIPS 2020 [Paper] [Official Code] [](#4) [](#4) [](#4) [](#4)
136 | - **Image Augmentations for GAN Training** arXiv 2020 [Paper] [](#7) [](#7) [](#7)
137 | - **Improved Consistency Regularization for GANs** AAAI 2021 [Paper] [](#1) [](#1) [](#1)
138 | - **DeceiveD: Adaptive pseudo augmentation for gan training with limited data** NeurIPS 2021 [Paper] [Official Code] [](#8) [](#8) [](#8)
139 | - **Data-efficient gan training beyond (just) augmentations: A lottery ticket perspective** NeurIPS 2021 [Paper] [Official Code] [](#9) [](#9) [](#9)
140 | - **Self-Supervised GANs with Label Augmentation** NeurIPS 2021 [Paper] [Official Code] [](#10) [](#10) [](#10)
141 | - **On Data Augmentation for GAN Training** TIP 2021 [Paper] [Official Code] [](#6) [](#6) [](#6)
142 | - **Adaptive Feature Interpolation for Low-Shot Image Generation** ECCV 2022 [Paper] [Official Code] [](#13) [](#13) [](#13)
143 | - **Training GANs with Diffusion** ICLR 2023 [Paper] [Official Code] [](#12) [](#12) [](#12)
144 | - **Faster and More Data-Efficient Training of Diffusion Models** arXiv 2023 [Paper] [](#11) [](#11) [](#11)
145 |
146 |
147 |
148 |
149 |
150 | ## Network Architectures
151 |
152 |
153 | Click to expand/collapse 11 works
154 |
155 | - **Towards faster and stabilized gan training for high-fidelity few-shot image synthesis** ICLR 2021 [Paper] [Official Code] [](#1) [](#1) [](#1)
156 | - **Data-efficient gan training beyond (just) augmentations: A lottery ticket perspective** NeurIPS 2021 [Paper] [Official Code] [](#0) [](#0) [](#0)
157 | - **Projected GANs Converge Faster** NeurIPS 2021 [Paper] [Official Code] [](#2) [](#2) [](#2)
158 | - **Prototype Memory and Attention Mechanisms for Few Shot Image Generation** ICLR 2022 [Paper] [Official Code] [](#3) [](#3) [](#3)
159 | - **Collapse by conditioning: Training class-conditional GANs with limited data** ICLR 2022 [Paper] [Official Code] [](#4) [](#4) [](#4)
160 | - **Ensembling Off-the-shelf Models for GAN Training** CVPR 2022 [Paper] [Official Code] [](#5) [](#5) [](#5)
161 | - **Hierarchical Context Aggregation for Few-Shot Generation** ICML 2022 [Paper] [Official Code] [](#9) [](#9) [](#9)
162 | - **Improving GANs with A Dynamic Discriminator** NeurIPS 2022 [Paper] [Official Code] [](#6) [](#6) [](#6)
163 | - **Data-Efficient GANs Training via Architectural Reconfiguration** CVPR 2023 [Paper] [Official Code] [](#8) [](#8) [](#8)
164 | - **Introducing editable and representative attributes for few-shot image generation** Engineering Applications of AI 2023 [Paper] [Official Code] [](#7) [](#7) [](#7)
165 | - **Toward a better image synthesis GAN framework for high-fidelity few-shot datasets via NAS and contrastive learning** Elsevier KBS 2023 [Paper] [Official Code] [](#10) [](#10) [](#10)
166 |
167 |
168 |
169 |
170 |
171 | ## Multi-Task Objectives
172 |
173 |
174 | Click to expand/collapse 25 works
175 |
176 | - **Image Augmentations for GAN Training** arXiv 2020 [Paper] [](#5) [](#5) [](#5)
177 | - **Regularizing generative adversarial networks under limited data** CVPR 2021 [Paper] [Official Code] [](#9) [](#9) [](#9)
178 | - **Contrastive Learning for Cross-domain Correspondence in Few-shot Image Generation** NeurIPS 2021-W [Paper] [](#3) [](#3) [](#3)
179 | - **Data-Efficient Instance Generation from Instance Discrimination** NeurIPS 2021 [Paper] [Official Code] [](#11) [](#11) [](#11)
180 | - **Diffusion-Decoding Models for Few-Shot Conditional Generation** NeurIPS 2021 [Paper] [Official Code] [](#20) [](#20) [](#20)
181 | - **Generative Co-training for Generative Adversarial Networks with Limited Data** AAAI 2022 [Paper] [Official Code] [](#13) [](#13) [](#13)
182 | - **Prototype Memory and Attention Mechanisms for Few Shot Image Generation** ICLR 2022 [Paper] [Official Code] [](#8) [](#8) [](#8)
183 | - **A Closer Look at Few-Shot Image Generation** CVPR 2022 [Paper] [](#0) [](#0) [](#0)
184 | - **Few-shot Image Generation with Mixup-based Distance Learning** ECCV 2022 [Paper] [Official Code] [](#15) [](#15) [](#15)
185 | - **Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs** ECCV 2022 [Paper] [Official Code] [](#16) [](#16) [](#16)
186 | - **Any-resolution Training for High-resolution Image Synthesis** ECCV 2022 [Paper] [Official Code] [](#19) [](#19) [](#19)
187 | - **Discriminator gradIent Gap Regularization for GAN Training with Limited Data** NeurIPS 2022 [Paper] [Official Code] [](#10) [](#10) [](#10)
188 | - **Masked Generative Adversarial Networks are Data-Efficient Generation Learners** NeurIPS 2022 [Paper] [](#14) [](#14) [](#14)
189 | - **Exploiting Knowledge Distillation for Few-Shot Image Generation** arXiv 2022 [Paper] [](#1) [](#1) [](#1)
190 | - **Few-shot Artistic Portraits Generation with Contrastive Transfer Learning** arXiv 2022 [Paper] [](#2) [](#2) [](#2)
191 | - **Few-Shot Diffusion Models** arXiv 2022 [Paper] [Official Code] [](#21) [](#21) [](#21)
192 | - **Few-shot image generation based on contrastive meta-learning generative adversarial network** Visual Computer 2022 [Paper] [](#24) [](#24) [](#24)
193 | - **Training GANs with Diffusion** ICLR 2023 [Paper] [Official Code] [](#7) [](#7) [](#7)
194 | - **Data Limited Image Generation via Knowledge Distillation** CVPR 2023 [Paper] [](#17) [](#17) [](#17)
195 | - **Adaptive IMLE for Few-shot Pretraining-free Generative Modelling** ICML 2023 [Paper] [Official Code] [](#23) [](#23) [](#23)
196 | - **Few-shot Image Generation via Masked Discrimination** arXiv 2023 [Paper] [](#4) [](#4) [](#4)
197 | - **Faster and More Data-Efficient Training of Diffusion Models** arXiv 2023 [Paper] [](#6) [](#6) [](#6)
198 | - **Towards high diversity and fidelity image synthesis under limited data** Information Sciences 2023 [Paper] [Official Code] [](#12) [](#12) [](#12)
199 | - **Regularizing Label-Augmented Generative Adversarial Networks Under Limited Data** IEEE Access 2023 [Paper] [](#18) [](#18) [](#18)
200 | - **Dynamically Masked Discriminator for Generative Adversarial Networks** arXiv 2023 [Paper] [](#22) [](#22) [](#22)
201 |
202 |
203 |
204 |
205 |
206 | ## Exploiting Frequency Components
207 |
208 | Click to expand/collapse 4 works
209 |
210 | - **Generative Co-training for Generative Adversarial Networks with Limited Data** AAAI 2022 [Paper] [Official Code] [](#3) [](#3)
211 | - **Frequency-Aware GAN for High-Fidelity Few-Shot Image Generation** ECCV 2022 [Paper] [Official Code] [](#1) [](#1)
212 | - **Improving GANs with A Dynamic Discriminator** NeurIPS 2022 [Paper] [Official Code] [](#0) [](#0)
213 | - **Exploiting Frequency Components for Training GANs under Limited Data** NeurIPS 2022 [Paper] [Official Code] [](#2) [](#2)
214 |
215 |
216 |
217 |
218 |
219 | ## Meta-learning
220 |
221 | Click to expand/collapse 17 works
222 |
223 | - **Data Augmentaion Generative Adversarial Networks** arXiv 2017 [Paper] [Official Code] [](#2) [](#2) [](#2)
224 | - **Few-shot Generative Modelling with Generative Matching Networks** AISTATS 2018 [Paper] [](#3) [](#3) [](#3)
225 | - **Few-shot Image Generation with Reptile** arXiv 2019 [Paper] [Official Code] [](#4) [](#4) [](#4)
226 | - **A domain adaptive few shot generation framework** arXiv 2020 [Paper] [](#5) [](#5) [](#5)
227 | - **Matching-based Few-shot Image Generation** ICME 2020 [Paper] [Official Code] [](#6) [](#6) [](#6)
228 | - **Fusing-and-Filling GAN for Few-shot Image Generation** ACM-MM 2020 [Paper] [Official Code] [](#7) [](#7) [](#7)
229 | - **Fusing Local Representations for Few-shot Image Generation** ICCV 2021 [Paper] [Official Code] [](#8) [](#8) [](#8)
230 | - **Fast Adaptive Meta-Learning for Few-Shot Image Generation** TMM 2021 [Paper] [Official Code] [](#10) [](#10) [](#10)
231 | - **Frequency-Aware GAN for High-Fidelity Few-Shot Image Generation** ECCV 2022 [Paper] [Official Code] [](#0) [](#0) [](#0)
232 | - **Towards Diverse Few-shot Image Generation with Sample-Specific Delta** ECCV 2022 [Paper] [Official Code] [](#9) [](#9) [](#9)
233 | - **Few-shot image generation based on contrastive meta-learning generative adversarial network** Visual Computer 2022 [Paper] [](#1) [](#1) [](#1)
234 | - **Few-shot Image Generation Using Discrete Content Representation** ACM MM 2022 [Paper] [](#12) [](#12) [](#12)
235 | - **The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image Generation** arXiv 2022 [Paper] [](#16) [](#16) [](#16)
236 | - **Where is My Spot? Few-shot Image Generation via Latent Subspace Optimization** CVPR 2023 [Paper] [Official Code] [](#13) [](#13) [](#13)
237 | - **Attribute Group Editing for Reliable Few-shot Image Generation** CVPR 2023 [Paper] [Official Code] [](#14) [](#14) [](#14)
238 | - **Adaptive multi-scale modulation generative adversarial network for few-shot image generation** Applied Intelligence 2023 [Paper] [](#11) [](#11) [](#11)
239 | - **Stable Attribute Group Editing for Reliable Few-shot Image Generation** arXiv 2023 [Paper] [Official Code] [](#15) [](#15) [](#15)
240 |
241 |
242 |
243 |
244 |
245 |
246 |
247 |
248 | ## Modeling Internal Patch Distribution
249 |
250 | Click to expand/collapse 8 works
251 |
252 | - **Learning a Generative Model from a Single Natural Image** ICCV 2019 [Paper] [Official Code] [](#0) [](#0) [](#0)
253 | - **Learning to generate samples from single images and videos** CVPR 2021-W [Paper] [Official Code] [](#1) [](#1) [](#1)
254 | - **Improved techniques for training single image gans** WACV 2021 [Paper] [Official Code] [](#2) [](#2) [](#2)
255 | - **Learning a Diffusion Model from a Single Natural Image** arXiv 2022 [Paper] [Official Code] [](#4) [](#4) [](#4)
256 | - **Learning and Blending the Internal Distributions of Single Images by Spatial Image-Identity Conditioning** arXiv 2022 [Paper] [](#5) [](#5) [](#5)
257 | - **Training Diffusion Models on a Single Image or Video** ICML 2023 [Paper] [Official Code] [](#6) [](#6) [](#6)
258 | - **A Single Image Denoising Diffusion Model** ICML 2023 [Paper] [Official Code] [](#7) [](#7) [](#7)
259 | - **Diverse Attribute Transfer for Few-Shot Image Synthesis** VISIGRAPP 2023 [Paper] [Official Code] [](#3) [](#3) [](#3)
260 |
261 |
262 |
263 |
264 |
265 |
266 | ## Citation
267 |
268 | If you find this repo useful, please cite our paper
269 |
@article{abdollahzadeh2023survey,
270 | title={A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot},
271 | author={Milad Abdollahzadeh and Touba Malekzadeh and Christopher T. H. Teo and Keshigeyan Chandrasegaran and Guimeng Liu and Ngai-Man Cheung},
272 | year={2023},
273 | eprint={2307.14397},
274 | archivePrefix={arXiv},
275 | primaryClass={cs.CV}
276 | }
277 |
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73 | - **Image Generation from Small Datasets via Batch Statistics Adaptation**
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107 | - **SINgle Image Editing with Text-to-Image Diffusion Models**
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108 | - **DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation**
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162 | - **Improving GANs with A Dynamic Discriminator**
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