mlsanityData to 7 Oct 2026

Few-shot Image Generation with Elastic Weight Consolidation

arXiv 2012.02780 ↗First posted 4 Dec 2020cs.CVcited by 122

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Cited by 24 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Few-shot image generation seeks to generate more data of a given domain, with only few available training examples. As it is unreasonable to expect to fully infer the distribution from just a few observations (e.g., emojis), we seek to leverage a large, related source domain as pretraining (e.g., human faces). Thus, we wish to preserve the diversity of the source domain, while adapting to the appearance of the target. We adapt a pretrained model, without introducing any additional parameters, to the few examples of the target domain. Crucially, we regularize the changes of the weights during this adaptation, in order to best preserve the information of the source dataset, while fitting the target. We demonstrate the effectiveness of our algorithm by generating high-quality results of different target domains, including those with extremely few examples (e.g., <10). We also analyze the performance of our method with respect to some important factors, such as the number of examples and the dissimilarity between the source and target domain.

Key facts. Cited by 122 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Dec 2020.

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Citing papers per year: 2021: 19; 2022: 35; 2023: 36; 2024: 23; 2025: 7; 2026: 2.

122 papers.

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  1. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  2. 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,099
  3. 2023Ablating Concepts in Text-to-Image Diffusion Modelscited by 312
  4. 2021Few-shot Image Generation via Cross-domain Correspondencecited by 206
  5. 2023DreamVideo: Composing Your Dream Videos with Customized Subject and Motioncited by 186
  6. 2021StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generatorscited by 164
  7. 2023A Recipe for Watermarking Diffusion Modelscited by 136
  8. 2023Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Modelscited by 131
  9. 2021Image-to-Image Translation: Methods and Applicationscited by 116
  10. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 110
  11. 2022Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignmentcited by 54
  12. 2023Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classificationcited by 54
38 more
  1. 2021JoJoGAN: One Shot Face Stylizationcited by 49
  2. 2022A Closer Look at Few-shot Image Generationcited by 49
  3. 2021StyleAlign: Analysis and Applications of Aligned StyleGAN Modelscited by 48
  4. 2022Modeling Image Composition for Complex Scene Generationcited by 43
  5. 2021One-Shot Generative Domain Adaptationcited by 41
  6. 2024FonTS: Text Rendering with Typography and Style Controlscited by 40
  7. 2021Sketch Your Own GANcited by 37
  8. 2022Diffusion Guided Domain Adaptation of Image Generatorscited by 35
  9. 2022State-of-the-Art in the Architecture, Methods and Applications of StyleGANcited by 34
  10. 2022Few-shot Image Generation via Adaptation-Aware Kernel Modulationcited by 33
  11. 2022DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Modelcited by 32
  12. 2022One-Shot Adaptation of GAN in Just One CLIPcited by 31
  13. 2022Fair Generative Models via Transfer Learningcited by 27
  14. 2022MyStyle: A Personalized Generative Priorcited by 26
  15. 2021Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspectivecited by 25
  16. 2022Rewriting Geometric Rules of a GANcited by 25
  17. 2022Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networkscited by 23
  18. 2024Data Attribution for Text-to-Image Models by Unlearning Synthesized Imagescited by 22
  19. 2022Few-shot Image Generation with Diffusion Modelscited by 21
  20. 2022A Comprehensive Survey on Data-Efficient GANs in Image Generationcited by 20
  21. 2022Generalized One-shot Domain Adaptation of Generative Adversarial Networkscited by 19
  22. 2022HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networkscited by 19
  23. 2023Exploring Incompatible Knowledge Transfer in Few-shot Image Generationcited by 19
  24. 2023A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shotcited by 19
  25. 2023Domain Re-Modulation for Few-Shot Generative Domain Adaptationcited by 18
  26. 2021Few-shot Image Generation with Mixup-based Distance Learningcited by 16
  27. 2022FreGAN: Exploiting Frequency Components for Training GANs under Limited Datacited by 16
  28. 2022Attribute Group Editing for Reliable Few-shot Image Generationcited by 15
  29. 2022DynaGAN: Dynamic Few-shot Adaptation of GANs to Multiple Domainscited by 15
  30. 2023DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Datacited by 15
  31. 2023Scenimefy: Learning to Craft Anime Scene via Semi-Supervised Image-to-Image Translationcited by 15
  32. 2022When, Why, and Which Pretrained GANs Are Useful?cited by 14
  33. 2023Domain Expansion of Image Generatorscited by 13
  34. 2025Fine-Tuning Visual Autoregressive Models for Subject-Driven Generationcited by 13
  35. 2022Few-shot Image Generation via Masked Discriminationcited by 12
  36. 2023WeditGAN: Few-Shot Image Generation via Latent Space Relocationcited by 12
  37. 2023LFS-GAN: Lifelong Few-Shot Image Generationcited by 11
  38. 2023Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaptioncited by 11

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