mlsanityData to 7 Oct 2026

Generative Adversarial Networks

arXiv 2203.00667 ↗First posted 1 Mar 2022cs.CV cs.LGcited by 5,603

Not on the map. Why?

Cited by 1,167 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the academia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation. We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. We then list some architecture-variant and loss-variant GANs that remedy the above challenges. Lastly, we present two utilization examples of GANs for real-world applications: Data augmentation and face images generation.

Key facts. Cited by 5,603 papers in the map (data through 7 Oct 2026). Builds on 87 of its 143 references that are in the map. First posted on arXiv 1 Mar 2022.

Builds on

87 of 143 references.

References · 87

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  6. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  7. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  8. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  9. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  10. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  11. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  12. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
38 more
  1. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  2. 2016Improved Techniques for Training GANscited by 2,938
  3. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  4. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  5. 2017Improved Training of Wasserstein GANscited by 2,223
  6. 2014Conditional Generative Adversarial Netscited by 2,152
  7. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  8. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  9. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
  10. 2016Progressive Neural Networkscited by 1,332
  11. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  12. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
  13. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
  14. 2021Alias-Free Generative Adversarial Networkscited by 1,181
  15. 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,176
  16. 2017CyCADA: Cycle-Consistent Adversarial Domain Adaptationcited by 1,079
  17. 2016Least Squares Generative Adversarial Networkscited by 1,000
  18. 2021StyleCLIP: Text-Driven Manipulation of StyleGAN Imagerycited by 990
  19. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 858
  20. 2018Self-Attention Generative Adversarial Networkscited by 809
  21. 2016Generative Adversarial Text to Image Synthesiscited by 735
  22. 2020Encoding in Style: a StyleGAN Encoder for Image-to-Image Translationcited by 727
  23. 2019Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?cited by 675
  24. 2018Which Training Methods for GANs do actually Converge?cited by 672
  25. 2017Unsupervised Image-to-Image Translation Networkscited by 602
  26. 2017Generative Adversarial Networks: An Overviewcited by 588
  27. 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 575
  28. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
  29. 2021Designing an Encoder for StyleGAN Image Manipulationcited by 542
  30. 2016Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networkscited by 541
  31. 2020Closed-Form Factorization of Latent Semantics in GANscited by 407
  32. 2020InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANscited by 393
  33. 2016SeqGAN: Sequence Generative Adversarial Nets with Policy Gradientcited by 375
  34. 2021GAN Inversion: A Surveycited by 366
  35. 2020Differentiable Augmentation for Data-Efficient GAN Trainingcited by 361
  36. 2019Image2StyleGAN++: How to Edit the Embedded Images?cited by 356
  37. 2019SinGAN: Learning a Generative Model from a Single Natural Imagecited by 348
  38. 2020StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flowscited by 348

50 most cited of 87 All 87 (JSON)

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Citing papers per year: 2022: 1253; 2023: 1664; 2024: 1150; 2025: 1046; 2026: 490.

5,603 papers.

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  1. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  2. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  3. 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,725
  4. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  5. 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,099
  6. 2023ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generationcited by 1,010
  7. 2022Diffusion Models in Vision: A Surveycited by 1,008
  8. 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 973
  9. 2023Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inferencecited by 826
  10. 2023Adversarial Diffusion Distillationcited by 741
  11. 2023Extracting Training Data from Diffusion Modelscited by 723
  12. 2022Point-E: A System for Generating 3D Point Clouds from Complex Promptscited by 694
38 more
  1. 2023Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generatorscited by 666
  2. 2023ModelScope Text-to-Video Technical Reportcited by 651
  3. 2023Muse: Text-To-Image Generation via Masked Generative Transformerscited by 640
  4. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Modelcited by 574
  5. 2022Paint by Example: Exemplar-based Image Editing with Diffusion Modelscited by 489
  6. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  7. 2022MaxViT: Multi-Axis Vision Transformercited by 476
  8. 2024Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Modelscited by 467
  9. 2023FateZero: Fusing Attentions for Zero-shot Text-based Video Editingcited by 462
  10. 2023VideoComposer: Compositional Video Synthesis with Motion Controllabilitycited by 461
  11. 2023Emergent Correspondence from Image Diffusioncited by 437
  12. 2023DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Modelscited by 431
  13. 2023Aligning Text-to-Image Models using Human Feedbackcited by 418
  14. 2024From Slow Bidirectional to Fast Autoregressive Video Diffusion Modelscited by 401
  15. 2023Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priorscited by 395
  16. 2023InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generationcited by 387
  17. 2023Shap-E: Generating Conditional 3D Implicit Functionscited by 379
  18. 2024One Step Diffusion via Shortcut Modelscited by 373
  19. 2023I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Modelscited by 343
  20. 2023Denoising Diffusion Models for Plug-and-Play Image Restorationcited by 335
  21. 2024OminiControl: Minimal and Universal Control for Diffusion Transformercited by 334
  22. 2025GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Controlcited by 325
  23. 2022Diffusion Models already have a Semantic Latent Spacecited by 324
  24. 2023ControlVideo: Training-free Controllable Text-to-Video Generationcited by 324
  25. 2023PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embeddingcited by 324
  26. 2023Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Modelscited by 323
  27. 2025Absolute Zero: Reinforced Self-play Reasoning with Zero Datacited by 323
  28. 2023DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Datacited by 322
  29. 2025From System 1 to System 2: A Survey of Reasoning Large Language Modelscited by 319
  30. 2023Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videoscited by 312
  31. 2023SeeSR: Towards Semantics-Aware Real-World Image Super-Resolutioncited by 311
  32. 2022Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusioncited by 309
  33. 2022CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformerscited by 305
  34. 2023Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generationcited by 300
  35. 2022EDGE: Editable Dance Generation From Musiccited by 284
  36. 2023Generative Novel View Synthesis with 3D-Aware Diffusion Modelscited by 273
  37. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  38. 2022Post-training Quantization on Diffusion Modelscited by 262

50 most cited of 5,603 All 5,603 (JSON)