mlsanityData to 7 Oct 2026

Triple Generative Adversarial Nets

arXiv 1703.02291 ↗First posted 7 Mar 2017cs.LG cs.CVcited by 59

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Abstract (arXiv)

Generative Adversarial Nets (GANs) have shown promise in image generation and semi-supervised learning (SSL). However, existing GANs in SSL have two problems: (1) the generator and the discriminator (i.e. the classifier) may not be optimal at the same time; and (2) the generator cannot control the semantics of the generated samples. The problems essentially arise from the two-player formulation, where a single discriminator shares incompatible roles of identifying fake samples and predicting labels and it only estimates the data without considering the labels. To address the problems, we present triple generative adversarial net (Triple-GAN), which consists of three players---a generator, a discriminator and a classifier. The generator and the classifier characterize the conditional distributions between images and labels, and the discriminator solely focuses on identifying fake image-label pairs. We design compatible utilities to ensure that the distributions characterized by the classifier and the generator both converge to the data distribution. Our results on various datasets demonstrate that Triple-GAN as a unified model can simultaneously (1) achieve the state-of-the-art classification results among deep generative models, and (2) disentangle the classes and styles of the input and transfer smoothly in the data space via interpolation in the latent space class-conditionally.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Mar 2017.

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Citing papers per year: 2020: 1; 2021: 18; 2022: 14; 2023: 13; 2024: 8; 2025: 5.

59 papers.

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  1. 2021A Survey on Deep Semi-supervised Learningcited by 250
  2. 2021Semi-supervised Semantic Segmentation with Directional Context-aware Consistencycited by 112
  3. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  4. 2021ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learningcited by 68
  5. 2022Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Modelcited by 58
  6. 2021CLDA: Contrastive Learning for Semi-Supervised Domain Adaptationcited by 52
  7. 2022Semi-Supervised and Unsupervised Deep Visual Learning: A Surveycited by 52
  8. 2021More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrievalcited by 38
  9. 2023Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labelscited by 37
  10. 2023Real-Fake: Effective Training Data Synthesis Through Distribution Matchingcited by 35
  11. 2022Why Are Conditional Generative Models Better Than Unconditional Ones?cited by 27
  12. 2023Toward Understanding Generative Data Augmentationcited by 25
38 more
  1. 2024Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directionscited by 20
  2. 2022Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentationcited by 15
  3. 2020Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Netscited by 11
  4. 2021Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluationcited by 11
  5. 2025CGMatch: A Different Perspective of Semi-supervised Learningcited by 11
  6. 2022medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Spacecited by 10
  7. 2024MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generationcited by 10
  8. 2022Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Surveycited by 8
  9. 2021FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learningcited by 7
  10. 2021Probabilistic Contrastive Learning for Domain Adaptationcited by 7
  11. 2021Learning Domain Invariant Representations by Joint Wasserstein Distance Minimizationcited by 6
  12. 2023Provable Unrestricted Adversarial Training without Compromise with Generalizabilitycited by 6
  13. 2022Multi-Granularity Distillation Scheme Towards Lightweight Semi-Supervised Semantic Segmentationcited by 5
  14. 2022Robust Imitation via Mirror Descent Inverse Reinforcement Learningcited by 5
  15. 2022Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classificationcited by 5
  16. 2023Semi-supervised Object Detection: A Survey on Recent Research and Progresscited by 5
  17. 2021Structured Latent Embeddings for Recognizing Unseen Classes in Unseen Domainscited by 4
  18. 2021Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Surveycited by 4
  19. 2022OSSGAN: Open-Set Semi-Supervised Image Generationcited by 4
  20. 2023Semi-Supervised Learning with Pseudo-Negative Labels for Image Classificationcited by 4
  21. 2023Semi-Supervised Image Captioning by Adversarially Propagating Labeled Datacited by 4
  22. 2024Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networkscited by 4
  23. 2021IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalancecited by 3
  24. 2021Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptationcited by 2
  25. 2021Universum GANs: Improving GANs through contradictionscited by 1
  26. 2021A Unified View of cGANs with and without Classifierscited by 1
  27. 2022Analysing the effectiveness of a generative model for semi-supervised medical image segmentationcited by 1
  28. 2023A Three-Player GAN for Super-Resolution in Magnetic Resonance Imagingcited by 1
  29. 2023Automatic Data Augmentation Learning using Bilevel Optimization for Histopathological Imagescited by 1
  30. 2024PATE-TripleGAN: Privacy-Preserving Image Synthesis with Gaussian Differential Privacycited by 1
  31. 2021Multiple Organ Failure Prediction with Classifier-Guided Generative Adversarial Imputation Networkscited by 0
  32. 20213N-GAN: Semi-Supervised Classification of X-Ray Images with a 3-Player Adversarial Frameworkcited by 0
  33. 2022Semi-Supervised Image-to-Image Translation using Latent Space Mappingcited by 0
  34. 2022Deep Low-Density Separation for Semi-Supervised Classificationcited by 0
  35. 2022Three-Player Game Training Dynamicscited by 0
  36. 2023A Distinct Unsupervised Reference Model From The Environment Helps Continual Learningcited by 0
  37. 2023A Sea-Land Clutter Classification Framework for Over-the-Horizon-Radar Based on Weighted Loss Semi-supervised GANcited by 0
  38. 2023A Chronological Survey of Theoretical Advancements in Generative Adversarial Networks for Computer Visioncited by 0

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