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Adversarial Feature Learning

arXiv 1605.09782 ↗First posted 31 May 2016cs.LG cs.AI cs.CVcited by 350

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Abstract (arXiv)

The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution. Intuitively, models trained to predict these semantic latent representations given data may serve as useful feature representations for auxiliary problems where semantics are relevant. However, in their existing form, GANs have no means of learning the inverse mapping -- projecting data back into the latent space. We propose Bidirectional Generative Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and demonstrate that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning.

Key facts. Cited by 350 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 May 2016.

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Citing papers per year: 2021: 133; 2022: 85; 2023: 67; 2024: 30; 2025: 22; 2026: 13.

350 papers.

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  1. 2021Vector-quantized Image Modeling with Improved VQGANcited by 656
  2. 2021GAN Inversion: A Surveycited by 366
  3. 2023Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioningcited by 254
  4. 2021A Survey on Deep Semi-supervised Learningcited by 250
  5. 2023A Cookbook of Self-Supervised Learningcited by 248
  6. 2022MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesiscited by 222
  7. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 221
  8. 2021Self-Supervised Pretraining of 3D Features on any Point-Cloudcited by 217
  9. 2021DetCo: Unsupervised Contrastive Learning for Object Detectioncited by 205
  10. 2022Diffusion-GAN: Training GANs with Diffusioncited by 203
  11. 2021Unsupervised Semantic Segmentation by Contrasting Object Mask Proposalscited by 190
  12. 2021Pretrained Transformers as Universal Computation Enginescited by 162
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  1. 2021EditGAN: High-Precision Semantic Image Editingcited by 156
  2. 2021SCARF: Self-Supervised Contrastive Learning using Random Feature Corruptioncited by 151
  3. 2021Efficient Visual Pretraining with Contrastive Detectioncited by 150
  4. 2021Illiterate DALL-E Learns to Composecited by 147
  5. 2022LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Taskscited by 146
  6. 2021Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizationcited by 141
  7. 2022Self-supervised Learning in Remote Sensing: A Reviewcited by 137
  8. 2023Denoising Diffusion Autoencoders are Unified Self-supervised Learnerscited by 128
  9. 2021Generative Adversarial Transformerscited by 120
  10. 2021Image-to-Image Translation: Methods and Applicationscited by 116
  11. 2021Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoderscited by 109
  12. 2021Generative Models as a Data Source for Multiview Representation Learningcited by 104
  13. 2021Contrastive Learning with Stronger Augmentationscited by 100
  14. 2021Towards the Generalization of Contrastive Self-Supervised Learningcited by 99
  15. 2021Large-scale Unsupervised Semantic Segmentationcited by 98
  16. 2021D2C: Diffusion-Denoising Models for Few-shot Conditional Generationcited by 96
  17. 2021Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editingcited by 91
  18. 2024Deconstructing Denoising Diffusion Models for Self-Supervised Learningcited by 89
  19. 2021Divide and Contrast: Self-supervised Learning from Uncurated Datacited by 85
  20. 2021Joint Learning of Localized Representations from Medical Images and Reportscited by 81
  21. 2022Object discovery and representation networkscited by 80
  22. 2021Self-supervised learning methods and applications in medical imaging analysis: A surveycited by 76
  23. 2023Diffusion Models as Masked Autoencoderscited by 71
  24. 2021Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelationcited by 65
  25. 2023Advances and Challenges in Meta-Learning: A Technical Reviewcited by 65
  26. 2021GAN-Supervised Dense Visual Alignmentcited by 61
  27. 2021Learning Representation for Clustering via Prototype Scattering and Positive Samplingcited by 58
  28. 2023Diffusion Models Beat GANs on Image Classificationcited by 57
  29. 2021Using latent space regression to analyze and leverage compositionality in GANscited by 55
  30. 2021In&Out : Diverse Image Outpainting via GAN Inversioncited by 55
  31. 2021The Power of Contrast for Feature Learning: A Theoretical Analysiscited by 53
  32. 2022Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifierscited by 53
  33. 2023An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initializationcited by 53
  34. 2021Adversarial Text-to-Image Synthesis: A Reviewcited by 52
  35. 2021Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learningcited by 52
  36. 2021Multimodal Image Synthesis and Editing: The Generative AI Eracited by 52
  37. 2022Semi-Supervised and Unsupervised Deep Visual Learning: A Surveycited by 52
  38. 2024Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connectionscited by 47

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