mlsanityData to 7 Oct 2026

Large Scale Adversarial Representation Learning

arXiv 1907.02544 ↗First posted 4 Jul 2019cs.CV cs.LG stat.MLcited by 238

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Cited by 26 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation quality translates to substantially improved representation learning performance. Our approach, BigBiGAN, builds upon the state-of-the-art BigGAN model, extending it to representation learning by adding an encoder and modifying the discriminator. We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as in unconditional image generation. Pretrained BigBiGAN models -- including image generators and encoders -- are available on TensorFlow Hub (https://tfhub.dev/s?publisher=deepmind&q=bigbigan).

Key facts. Cited by 238 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jul 2019.

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Citing papers per year: 2021: 93; 2022: 51; 2023: 51; 2024: 26; 2025: 10; 2026: 7.

238 papers.

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  1. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  2. 2021Vector-quantized Image Modeling with Improved VQGANcited by 656
  3. 2023Muse: Text-To-Image Generation via Masked Generative Transformerscited by 640
  4. 2021Label-Efficient Semantic Segmentation with Diffusion Modelscited by 495
  5. 2021GAN Inversion: A Surveycited by 366
  6. 2021Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentationcited by 268
  7. 2021Unsupervised Representation Learning for Time Series with Temporal Neighborhood Codingcited by 223
  8. 2022MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesiscited by 222
  9. 2021DetCo: Unsupervised Contrastive Learning for Object Detectioncited by 205
  10. 2021Unsupervised Semantic Segmentation by Contrasting Object Mask Proposalscited by 190
  11. 2021BEVT: BERT Pretraining of Video Transformerscited by 169
  12. 2021A Good Image Generator Is What You Need for High-Resolution Video Synthesiscited by 162
38 more
  1. 2021Efficient Self-supervised Vision Transformers for Representation Learningcited by 149
  2. 2021Illiterate DALL-E Learns to Composecited by 147
  3. 2022Self-supervised Learning in Remote Sensing: A Reviewcited by 137
  4. 2023Denoising Diffusion Autoencoders are Unified Self-supervised Learnerscited by 128
  5. 2021Generative Models as a Data Source for Multiview Representation Learningcited by 104
  6. 2021Pre-training without Natural Imagescited by 100
  7. 2021Contrastive Learning with Stronger Augmentationscited by 100
  8. 2021Instance Localization for Self-supervised Detection Pretrainingcited by 99
  9. 2021SiT: Self-supervised vIsion Transformercited by 99
  10. 2021Towards the Generalization of Contrastive Self-Supervised Learningcited by 99
  11. 2021Large-scale Unsupervised Semantic Segmentationcited by 98
  12. 2023Return of Unconditional Generation: A Self-supervised Representation Generation Methodcited by 95
  13. 2021Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editingcited by 91
  14. 2024Deconstructing Denoising Diffusion Models for Self-Supervised Learningcited by 89
  15. 2021Repurposing GANs for One-shot Semantic Part Segmentationcited by 88
  16. 2021Divide and Contrast: Self-supervised Learning from Uncurated Datacited by 85
  17. 2021ReSSL: Relational Self-Supervised Learning with Weak Augmentationcited by 83
  18. 2021Weakly Supervised Contrastive Learningcited by 81
  19. 2021Joint Learning of Localized Representations from Medical Images and Reportscited by 81
  20. 2023GIVT: Generative Infinite-Vocabulary Transformerscited by 80
  21. 2023SODA: Bottleneck Diffusion Models for Representation Learningcited by 76
  22. 2023Diffusion Models as Masked Autoencoderscited by 71
  23. 2023Learning Vision from Models Rivals Learning Vision from Datacited by 71
  24. 2023Advances and Challenges in Meta-Learning: A Technical Reviewcited by 65
  25. 2021Spatially Consistent Representation Learningcited by 64
  26. 2021GAN-Supervised Dense Visual Alignmentcited by 61
  27. 2021Improved Transformer for High-Resolution GANscited by 59
  28. 2021Data-Efficient Instance Generation from Instance Discriminationcited by 58
  29. 2021Learning Representation for Clustering via Prototype Scattering and Positive Samplingcited by 58
  30. 2021Diffusion-Based Representation Learningcited by 57
  31. 2023Diffusion Models Beat GANs on Image Classificationcited by 57
  32. 2021Using latent space regression to analyze and leverage compositionality in GANscited by 55
  33. 2022Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANscited by 55
  34. 2022MARLIN: Masked Autoencoder for facial video Representation LearnINgcited by 55
  35. 2024Diffusion Models and Representation Learning: A Surveycited by 53
  36. 2021Adversarial Text-to-Image Synthesis: A Reviewcited by 52
  37. 2021Low-Rank Subspaces in GANscited by 52
  38. 2022Semi-Supervised and Unsupervised Deep Visual Learning: A Surveycited by 52

50 most cited of 238 All 238 (JSON)