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NIPS 2016 Tutorial: Generative Adversarial Networks

arXiv 1701.00160 ↗First posted 31 Dec 2016cs.LGcited by 284

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Abstract (arXiv)

This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs). The tutorial describes: (1) Why generative modeling is a topic worth studying, (2) how generative models work, and how GANs compare to other generative models, (3) the details of how GANs work, (4) research frontiers in GANs, and (5) state-of-the-art image models that combine GANs with other methods. Finally, the tutorial contains three exercises for readers to complete, and the solutions to these exercises.

Key facts. Cited by 284 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Dec 2016.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 1; 2021: 82; 2022: 80; 2023: 57; 2024: 29; 2025: 22; 2026: 12.

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  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2023GAIA-1: A Generative World Model for Autonomous Drivingcited by 618
  3. 2021UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Modelscited by 133
  4. 2021Image-to-Image Translation: Methods and Applicationscited by 116
  5. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  6. 2021An Introduction to Deep Generative Modelingcited by 104
  7. 2021FitVid: Overfitting in Pixel-Level Video Predictioncited by 86
  8. 2024Distilling Diffusion Models into Conditional GANscited by 78
  9. 2023NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Modelscited by 76
  10. 2021Domain Invariant Representation Learning with Domain Density Transformationscited by 53
  11. 2021Optimal Representations for Covariate Shiftcited by 51
  12. 2023Vector Quantized Time Series Generation with a Bidirectional Prior Modelcited by 49
38 more
  1. 2023Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEscited by 48
  2. 2018Sample-Efficient Imitation Learning via Generative Adversarial Netscited by 36
  3. 2021KL Guided Domain Adaptationcited by 35
  4. 2024AI and the Problem of Knowledge Collapsecited by 34
  5. 2021Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstructioncited by 33
  6. 2023Free Lunch for Domain Adversarial Training: Environment Label Smoothingcited by 33
  7. 2021Geometric Entropic Explorationcited by 32
  8. 2022Studying Bias in GANs through the Lens of Racecited by 31
  9. 2024Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defensescited by 30
  10. 2022Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysiscited by 29
  11. 2022Learning to simulate realistic limit order book markets from data as a World Agentcited by 27
  12. 2023Conditional Optimal Transport on Function Spacescited by 26
  13. 2021Learning by Planning: Language-Guided Global Image Editingcited by 25
  14. 2024At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligencecited by 25
  15. 2021MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulationcited by 24
  16. 2022A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysiscited by 24
  17. 2022AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Imagescited by 24
  18. 2021A survey on Variational Autoencoders from a GreenAI perspectivecited by 22
  19. 2022MIDMs: Matching Interleaved Diffusion Models for Exemplar-based Image Translationcited by 22
  20. 2022Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generationcited by 22
  21. 2021Creativity and Machine Learning: A Surveycited by 21
  22. 2021A Neural Tangent Kernel Perspective of GANscited by 21
  23. 2022A State-Distribution Matching Approach to Non-Episodic Reinforcement Learningcited by 21
  24. 2023Unifying GANs and Score-Based Diffusion as Generative Particle Modelscited by 21
  25. 2024Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directionscited by 20
  26. 2022Do Quantum Circuit Born Machines Generalize?cited by 19
  27. 2023VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distributioncited by 17
  28. 2023Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Promptscited by 17
  29. 2022Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimizationcited by 16
  30. 2020Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learningcited by 15
  31. 2021Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guaranteescited by 15
  32. 2021Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancementcited by 15
  33. 2022A survey on GANs for computer vision: Recent research, analysis and taxonomycited by 15
  34. 2022UDoc-GAN: Unpaired Document Illumination Correction with Background Light Priorcited by 15
  35. 2023A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Modelscited by 15
  36. 2024Dealing with unbounded gradients in stochastic saddle-point optimizationcited by 15
  37. 2021Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Modelcited by 14
  38. 2022Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learningcited by 14

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