mlsanityData to 7 Oct 2026

Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks

arXiv 2110.08398 ↗First posted 15 Oct 2021cs.CVcited by 68

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Abstract (arXiv)

We present a new method for one shot domain adaptation. The input to our method is trained GAN that can produce images in domain A and a single reference image I_B from domain B. The proposed algorithm can translate any output of the trained GAN from domain A to domain B. There are two main advantages of our method compared to the current state of the art: First, our solution achieves higher visual quality, e.g. by noticeably reducing overfitting. Second, our solution allows for more degrees of freedom to control the domain gap, i.e. what aspects of image I_B are used to define the domain B. Technically, we realize the new method by building on a pre-trained StyleGAN generator as GAN and a pre-trained CLIP model for representing the domain gap. We propose several new regularizers for controlling the domain gap to optimize the weights of the pre-trained StyleGAN generator to output images in domain B instead of domain A. The regularizers prevent the optimization from taking on too many attributes of the single reference image. Our results show significant visual improvements over the state of the art as well as multiple applications that highlight improved control.

Key facts. Cited by 68 papers in the map (data through 7 Oct 2026). Builds on 38 of its 49 references that are in the map. First posted on arXiv 15 Oct 2021.

Builds on

38 of 49 references.

References · 38

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  4. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  5. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  6. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
  7. 2021Alias-Free Generative Adversarial Networkscited by 1,181
  8. 2021StyleCLIP: Text-Driven Manipulation of StyleGAN Imagerycited by 990
  9. 2020Encoding in Style: a StyleGAN Encoder for Image-to-Image Translationcited by 727
  10. 2020pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesiscited by 708
  11. 2019Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?cited by 675
  12. 2020GANSpace: Discovering Interpretable GAN Controlscited by 612
26 more
  1. 2021Designing an Encoder for StyleGAN Image Manipulationcited by 542
  2. 2020In-Domain GAN Inversion for Real Image Editingcited by 420
  3. 2019Contrastive Adaptation Network for Unsupervised Domain Adaptationcited by 407
  4. 2020InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANscited by 393
  5. 2020Differentiable Augmentation for Data-Efficient GAN Trainingcited by 361
  6. 2019Image2StyleGAN++: How to Edit the Embedded Images?cited by 356
  7. 2020StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flowscited by 348
  8. 2020StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generationcited by 331
  9. 2016Domain Separation Networkscited by 317
  10. 2016Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networkscited by 307
  11. 2020StyleRig: Rigging StyleGAN for 3D Control over Portrait Imagescited by 267
  12. 2021DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effortcited by 264
  13. 2019Few-Shot Unsupervised Image-to-Image Translationcited by 222
  14. 2021Few-shot Image Generation via Cross-domain Correspondencecited by 206
  15. 2020Editing in Style: Uncovering the Local Semantics of GANscited by 183
  16. 2019Seeing What a GAN Cannot Generatecited by 148
  17. 2020Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANscited by 132
  18. 2020FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptationcited by 124
  19. 2020Adversarial Feature Hallucination Networks for Few-Shot Learningcited by 110
  20. 2020Improved StyleGAN Embedding: Where are the Good Latents?cited by 88
  21. 2021Repurposing GANs for One-shot Semantic Part Segmentationcited by 88
  22. 2019Emergence of Object Segmentation in Perturbed Generative Modelscited by 57
  23. 2019Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labelingcited by 53
  24. 2021Labels4Free: Unsupervised Segmentation using StyleGANcited by 53
  25. 2020PIE: Portrait Image Embedding for Semantic Controlcited by 41
  26. 2021StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulationcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 20; 2023: 26; 2024: 12; 2025: 5; 2026: 1.

68 papers.

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  1. 2023ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAscited by 194
  2. 2022Diffusion-based Image Translation using Disentangled Style and Content Representationcited by 148
  3. 2021CLIP2StyleGAN: Unsupervised Extraction of StyleGAN Edit Directionscited by 78
  4. 2022Stitch it in Time: GAN-Based Facial Editing of Real Videoscited by 68
  5. 2022Third Time's the Charm? Image and Video Editing with StyleGAN3cited by 54
  6. 2021JoJoGAN: One Shot Face Stylizationcited by 49
  7. 2021One-Shot Generative Domain Adaptationcited by 41
  8. 2021Image-Based CLIP-Guided Essence Transfercited by 39
  9. 20233DAvatarGAN: Bridging Domains for Personalized Editable Avatarscited by 38
  10. 2022State-of-the-Art in the Architecture, Methods and Applications of StyleGANcited by 34
  11. 2022One-Shot Adaptation of GAN in Just One CLIPcited by 31
  12. 2023DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulationcited by 27
38 more
  1. 2022No Token Left Behind: Explainability-Aided Image Classification and Generationcited by 25
  2. 2024UnZipLoRA: Separating Content and Style from a Single Imagecited by 24
  3. 2022Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networkscited by 23
  4. 2025Personalized Image Generation with Deep Generative Models: A Decade Surveycited by 20
  5. 2022Generalized One-shot Domain Adaptation of Generative Adversarial Networkscited by 19
  6. 2022HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networkscited by 19
  7. 2023A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shotcited by 19
  8. 2023Domain Re-Modulation for Few-Shot Generative Domain Adaptationcited by 18
  9. 2022FEAT: Face Editing with Attentioncited by 16
  10. 2022clip2latent: Text driven sampling of a pre-trained StyleGAN using denoising diffusion and CLIPcited by 16
  11. 2022DynaGAN: Dynamic Few-shot Adaptation of GANs to Multiple Domainscited by 15
  12. 2023DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Datacited by 15
  13. 2023Scenimefy: Learning to Craft Anime Scene via Semi-Supervised Image-to-Image Translationcited by 15
  14. 2023Partial Identifiability for Domain Adaptationcited by 14
  15. 2024One-Shot Structure-Aware Stylized Image Synthesiscited by 14
  16. 2024Deformable One-shot Face Stylization via DINO Semantic Guidancecited by 14
  17. 2023Domain Expansion of Image Generatorscited by 13
  18. 2022Few-shot Image Generation via Masked Discriminationcited by 12
  19. 2023WeditGAN: Few-Shot Image Generation via Latent Space Relocationcited by 12
  20. 2022What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputscited by 11
  21. 2022CtlGAN: Few-shot Artistic Portraits Generation with Contrastive Transfer Learningcited by 10
  22. 2022AgileAvatar: Stylized 3D Avatar Creation via Cascaded Domain Bridgingcited by 10
  23. 2022StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptationcited by 10
  24. 2023Portrait Diffusion: Training-free Face Stylization with Chain-of-Paintingcited by 10
  25. 2022Video2StyleGAN: Disentangling Local and Global Variations in a Videocited by 8
  26. 2023StyO: Stylize Your Face in Only One-shotcited by 6
  27. 2023One-for-All: Towards Universal Domain Translation with a Single StyleGANcited by 6
  28. 2023Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generationcited by 5
  29. 2025Subject or Style: Adaptive and Training-Free Mixture of LoRAscited by 5
  30. 2023AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptationcited by 4
  31. 2024Representative Feature Extraction During Diffusion Process for Sketch Extraction with One Examplecited by 4
  32. 2024Stylized Face Sketch Extraction via Generative Prior with Limited Datacited by 4
  33. 2024LeGO: Leveraging a Surface Deformation Network for Animatable Stylized Face Generation with One Examplecited by 4
  34. 2024MagicMirror: Fast and High-Quality Avatar Generation with a Constrained Search Spacecited by 4
  35. 2024GCA-3D: Towards Generalized and Consistent Domain Adaptation of 3D Generatorscited by 4
  36. 2022MultiStyleGAN: Multiple One-shot Image Stylizations using a Single GANcited by 3
  37. 2024Task2Box: Box Embeddings for Modeling Asymmetric Task Relationshipscited by 3
  38. 2024One-shot Generative Domain Adaptation in 3D GANscited by 3

50 most cited of 68 All 68 (JSON)