mlsanityData to 7 Oct 2026

Coupled Generative Adversarial Networks

arXiv 1606.07536 ↗First posted 24 Jun 2016cs.CVcited by 216

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Abstract (arXiv)

We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast to the existing approaches, which require tuples of corresponding images in different domains in the training set, CoGAN can learn a joint distribution without any tuple of corresponding images. It can learn a joint distribution with just samples drawn from the marginal distributions. This is achieved by enforcing a weight-sharing constraint that limits the network capacity and favors a joint distribution solution over a product of marginal distributions one. We apply CoGAN to several joint distribution learning tasks, including learning a joint distribution of color and depth images, and learning a joint distribution of face images with different attributes. For each task it successfully learns the joint distribution without any tuple of corresponding images. We also demonstrate its applications to domain adaptation and image transformation.

Key facts. Cited by 216 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jun 2016.

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Citing papers per year: 2021: 86; 2022: 56; 2023: 32; 2024: 17; 2025: 19; 2026: 6.

216 papers.

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  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 221
  3. 2021The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalizationcited by 131
  4. 2021Deep Subdomain Adaptation Network for Image Classificationcited by 119
  5. 2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
  6. 2021Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptationcited by 83
  7. 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forwardcited by 82
  8. 2022Deep Clustering: A Comprehensive Surveycited by 78
  9. 2021DG-Font: Deformable Generative Networks for Unsupervised Font Generationcited by 70
  10. 2022Synthetic Data -- what, why and how?cited by 66
  11. 2021Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Imagescited by 58
  12. 2021Reconsidering Representation Alignment for Multi-view Clusteringcited by 57
38 more
  1. 2022Transferability in Deep Learning: A Surveycited by 57
  2. 2021Domain Invariant Representation Learning with Domain Density Transformationscited by 53
  3. 2021Dynamic Weighted Learning for Unsupervised Domain Adaptationcited by 53
  4. 2021Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptationcited by 47
  5. 2021Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applicationscited by 47
  6. 2023RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Modelcited by 47
  7. 2021Federated Multi-Target Domain Adaptationcited by 44
  8. 2022Diff-Font: Diffusion Model for Robust One-Shot Font Generationcited by 40
  9. 2021DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptationcited by 37
  10. 2021Curriculum Graph Co-Teaching for Multi-Target Domain Adaptationcited by 37
  11. 2021Contrastive Domain Adaptationcited by 36
  12. 2025Model Adaptation: Unsupervised Domain Adaptation without Source Datacited by 34
  13. 2021Visualizing Adapted Knowledge in Domain Transfercited by 32
  14. 2021GANs Can Play Lottery Tickets Toocited by 32
  15. 2022Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminatorcited by 32
  16. 2021A Survey of Unsupervised Domain Adaptation for Visual Recognitioncited by 31
  17. 2021A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networkscited by 30
  18. 2022TeST: Test-time Self-Training under Distribution Shiftcited by 29
  19. 2023Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Datacited by 29
  20. 2022Cross-Modal 3D Shape Generation and Manipulationcited by 27
  21. 2022Robust Imitation Learning against Variations in Environment Dynamicscited by 26
  22. 2023Semantic Image Segmentation: Two Decades of Researchcited by 26
  23. 2023A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRUcited by 25
  24. 2022DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentationcited by 22
  25. 2021Cross-View Exocentric to Egocentric Video Synthesiscited by 20
  26. 2021ZiGAN: Fine-grained Chinese Calligraphy Font Generation via a Few-shot Style Transfer Approachcited by 20
  27. 2021ACR-Pose: Adversarial Canonical Representation Reconstruction Network for Category Level 6D Object Pose Estimationcited by 20
  28. 2021Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Surveycited by 19
  29. 2022Multi-Representation Adaptation Network for Cross-domain Image Classificationcited by 19
  30. 2021GAN-based Reactive Motion Synthesis with Class-aware Discriminators for Human-human Interactioncited by 18
  31. 2022Avocodo: Generative Adversarial Network for Artifact-free Vocodercited by 18
  32. 2022VecGAN: Image-to-Image Translation with Interpretable Latent Directionscited by 17
  33. 2025DiffV2IR: Visible-to-Infrared Diffusion Model via Vision-Language Understandingcited by 16
  34. 2021See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptationcited by 15
  35. 2022Unsupervised Structure-Texture Separation Network for Oracle Character Recognitioncited by 15
  36. 2022AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalizationcited by 15
  37. 2023OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancingcited by 14
  38. 2021Efficient Conditional GAN Transfer with Knowledge Propagation across Classescited by 13

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