mlsanityData to 7 Oct 2026

A Large-Scale Study on Regularization and Normalization in GANs

arXiv 1807.04720 ↗First posted 12 Jul 2018cs.LG stat.MLcited by 26

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Abstract (arXiv)

Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant number of hyperparameter tuning, neural architecture engineering, and a non-trivial amount of "tricks". The success in many practical applications coupled with the lack of a measure to quantify the failure modes of GANs resulted in a plethora of proposed losses, regularization and normalization schemes, as well as neural architectures. In this work we take a sober view of the current state of GANs from a practical perspective. We discuss and evaluate common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jul 2018.

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Citing papers per year: 2019: 2; 2020: 2; 2021: 7; 2022: 7; 2023: 3; 2024: 1; 2025: 3; 2026: 1.

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  1. 2021Projected GANs Converge Fastercited by 204
  2. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  3. 2022Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guaranteescited by 42
  4. 2019SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewardscited by 28
  5. 2022Sources of Irreproducibility in Machine Learning: A Reviewcited by 28
  6. 2020Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learningcited by 15
  7. 2022A survey on GANs for computer vision: Recent research, analysis and taxonomycited by 15
  8. 2023SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Modelscited by 11
  9. 2025Disentangling Length Bias In Preference Learning Via Response-Conditioned Modelingcited by 6
  10. 2020Self-Imitation Learning for Robot Tasks with Sparse and Delayed Rewardscited by 4
  11. 2021Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared Discriminatorcited by 4
  12. 2021PeaceGAN: A GAN-based Multi-Task Learning Method for SAR Target Image Generation with a Pose Estimator and an Auxiliary Classifiercited by 4
14 more
  1. 2021Iterative Alignment Flowscited by 4
  2. 2022Cooperative Distribution Alignment via JSD Upper Boundcited by 4
  3. 2023Towards Practical Non-Adversarial Distribution Matchingcited by 2
  4. 2022Adversarial synthesis based data-augmentation for code-switched spoken language identificationcited by 1
  5. 2022Regularized Soft Actor-Critic for Behavior Transfer Learningcited by 1
  6. 2024SEAL: SEmantic-Augmented Imitation Learning via Language Modelcited by 1
  7. 2025Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimalitycited by 1
  8. 2026FAIL: Flow Matching Adversarial Imitation Learning for Image Generationcited by 1
  9. 2019Relational Mimic for Visual Adversarial Imitation Learningcited by 0
  10. 2021Deep Consensus Learningcited by 0
  11. 2021Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image Recognitioncited by 0
  12. 2022RCC-GAN: Regularized Compound Conditional GAN for Large-Scale Tabular Data Synthesiscited by 0
  13. 2023Penalty Gradient Normalization for Generative Adversarial Networkscited by 0
  14. 2025Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularizationcited by 0