mlsanityData to 7 Oct 2026

Few-shot Image Generation via Cross-domain Correspondence

arXiv 2104.06820 ↗First posted 13 Apr 2021cs.CV cs.GR cs.LGcited by 206

Not on the map. Why?

Cited by 36 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Training generative models, such as GANs, on a target domain containing limited examples (e.g., 10) can easily result in overfitting. In this work, we seek to utilize a large source domain for pretraining and transfer the diversity information from source to target. We propose to preserve the relative similarities and differences between instances in the source via a novel cross-domain distance consistency loss. To further reduce overfitting, we present an anchor-based strategy to encourage different levels of realism over different regions in the latent space. With extensive results in both photorealistic and non-photorealistic domains, we demonstrate qualitatively and quantitatively that our few-shot model automatically discovers correspondences between source and target domains and generates more diverse and realistic images than previous methods.

Key facts. Cited by 206 papers in the map (data through 7 Oct 2026). Builds on 37 of its 42 references that are in the map. First posted on arXiv 13 Apr 2021.

Builds on

37 of 42 references.

References · 37

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  11. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  12. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
25 more
  1. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  2. 2016Matching Networks for One Shot Learningcited by 2,246
  3. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  4. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
  5. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  6. 2019Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?cited by 675
  7. 2020NVAE: A Deep Hierarchical Variational Autoencodercited by 635
  8. 2020Reliable Fidelity and Diversity Metrics for Generative Modelscited by 425
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  10. 2017Toward Multimodal Image-to-Image Translationcited by 295
  11. 2019Few-Shot Unsupervised Image-to-Image Translationcited by 222
  12. 2016Texture Networks: Feed-forward Synthesis of Textures and Stylized Imagescited by 173
  13. 2015Inverting Visual Representations with Convolutional Networkscited by 168
  14. 2019Mode Seeking Generative Adversarial Networks for Diverse Image Synthesiscited by 138
  15. 2018Transferring GANs: generating images from limited datacited by 134
  16. 2020Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANscited by 132
  17. 2020Few-shot Image Generation with Elastic Weight Consolidationcited by 122
  18. 2019MineGAN: effective knowledge transfer from GANs to target domains with few imagescited by 106
  19. 2019Image Generation From Small Datasets via Batch Statistics Adaptationcited by 102
  20. 2017One-Sided Unsupervised Domain Mappingcited by 91
  21. 2019Diversity-Sensitive Conditional Generative Adversarial Networkscited by 77
  22. 2020COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encodercited by 39
  23. 2018Dist-GAN: An Improved GAN using Distance Constraintscited by 26
  24. 2020Semi-supervised Learning for Few-shot Image-to-Image Translationcited by 25
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 16; 2022: 54; 2023: 61; 2024: 36; 2025: 28; 2026: 11.

206 papers.

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  1. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  2. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,591
  3. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
  4. 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,099
  5. 2023Ablating Concepts in Text-to-Image Diffusion Modelscited by 312
  6. 2021HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editingcited by 208
  7. 2023ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAscited by 194
  8. 2021StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generatorscited by 164
  9. 2022Diffusion-based Image Translation using Disentangled Style and Content Representationcited by 148
  10. 2023A Recipe for Watermarking Diffusion Modelscited by 136
  11. 2023Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Modelscited by 131
  12. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 110
38 more
  1. 2021Ensembling Off-the-shelf Models for GAN Trainingcited by 84
  2. 2022Visual Prompt Tuning for Generative Transfer Learningcited by 75
  3. 2021Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networkscited by 68
  4. 2022Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfercited by 66
  5. 2023Few-Shot Defect Image Generation via Defect-Aware Feature Manipulationcited by 66
  6. 2022Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignmentcited by 54
  7. 2023Custom-Edit: Text-Guided Image Editing with Customized Diffusion Modelscited by 54
  8. 2021JoJoGAN: One Shot Face Stylizationcited by 49
  9. 2022A Closer Look at Few-shot Image Generationcited by 49
  10. 2021StyleAlign: Analysis and Applications of Aligned StyleGAN Modelscited by 48
  11. 2022Modeling Image Composition for Complex Scene Generationcited by 43
  12. 2021One-Shot Generative Domain Adaptationcited by 41
  13. 2021Image-Based CLIP-Guided Essence Transfercited by 39
  14. 2024An image speaks a thousand words, but can everyone listen? On image transcreation for cultural relevancecited by 36
  15. 2022Diffusion Guided Domain Adaptation of Image Generatorscited by 35
  16. 2022State-of-the-Art in the Architecture, Methods and Applications of StyleGANcited by 34
  17. 2022Few-shot Image Generation via Adaptation-Aware Kernel Modulationcited by 33
  18. 2022DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Modelcited by 32
  19. 2022One-Shot Adaptation of GAN in Just One CLIPcited by 31
  20. 2024Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generationcited by 31
  21. 2022Fair Generative Models via Transfer Learningcited by 27
  22. 2023MaskSketch: Unpaired Structure-guided Masked Image Generationcited by 27
  23. 2023Zero-shot Generative Model Adaptation via Image-specific Prompt Learningcited by 27
  24. 2022MyStyle: A Personalized Generative Priorcited by 26
  25. 2024SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuningcited by 26
  26. 2022Integrative Few-Shot Learning for Classification and Segmentationcited by 25
  27. 2022Rewriting Geometric Rules of a GANcited by 25
  28. 2023Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Modelscited by 24
  29. 2024UnZipLoRA: Separating Content and Style from a Single Imagecited by 24
  30. 2022Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networkscited by 23
  31. 2024Unseen Visual Anomaly Generationcited by 23
  32. 2022Few-shot Image Generation with Diffusion Modelscited by 21
  33. 2024AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusioncited by 21
  34. 2022A Comprehensive Survey on Data-Efficient GANs in Image Generationcited by 20
  35. 2022Generalized One-shot Domain Adaptation of Generative Adversarial Networkscited by 19
  36. 2022HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networkscited by 19
  37. 2023Exploring Incompatible Knowledge Transfer in Few-shot Image Generationcited by 19
  38. 2023A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shotcited by 19

50 most cited of 206 All 206 (JSON)