mlsanityData to 7 Oct 2026

Fisher GAN

arXiv 1705.09675 ↗First posted 26 May 2017cs.LG stat.MLcited by 22

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Abstract (arXiv)

Generative Adversarial Networks (GANs) are powerful models for learning complex distributions. Stable training of GANs has been addressed in many recent works which explore different metrics between distributions. In this paper we introduce Fisher GAN which fits within the Integral Probability Metrics (IPM) framework for training GANs. Fisher GAN defines a critic with a data dependent constraint on its second order moments. We show in this paper that Fisher GAN allows for stable and time efficient training that does not compromise the capacity of the critic, and does not need data independent constraints such as weight clipping. We analyze our Fisher IPM theoretically and provide an algorithm based on Augmented Lagrangian for Fisher GAN. We validate our claims on both image sample generation and semi-supervised classification using Fisher GAN.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 May 2017.

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Citing papers per year: 2021: 5; 2022: 7; 2023: 3; 2024: 4; 2025: 2; 2026: 1.

22 papers.

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  1. 2024Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Modelscited by 552
  2. 2022Generative Multiplane Images: Making a 2D GAN 3D-Awarecited by 57
  3. 2022StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesiscited by 38
  4. 2021Realizing GANs via a Tunable Loss Functioncited by 10
  5. 2022DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Datacited by 7
  6. 2021Training Generative Adversarial Networks in One Stagecited by 6
  7. 2023Addressing GAN Training Instabilities via Tunable Classification Lossescited by 5
  8. 2021Multi-Discriminator Sobolev Defense-GAN Against Adversarial Attacks for End-to-End Speech Systemscited by 4
  9. 2022Robust Vector Quantized-Variational Autoencodercited by 4
  10. 2021Bi-Discriminator Class-Conditional Tabular GANcited by 3
  11. 2026AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Losscited by 3
  12. 2021Adversarial Machine Learning in Text Analysis and Generationcited by 2
10 more
  1. 2024CHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned Normalizationcited by 2
  2. 2022Learning with Stochastic Orderscited by 1
  3. 2023Hoeffding's Inequality for Markov Chains under Generalized Concentrability Conditioncited by 1
  4. 2024CF-GO-Net: A Universal Distribution Learner via Characteristic Function Networks with Graph Optimizerscited by 1
  5. 2025Scalable Sobolev IPM for Probability Measures on a Graphcited by 1
  6. 2022RCC-GAN: Regularized Compound Conditional GAN for Large-Scale Tabular Data Synthesiscited by 0
  7. 2022RSD-GAN: Regularized Sobolev Defense GAN Against Speech-to-Text Adversarial Attackscited by 0
  8. 2023A Chronological Survey of Theoretical Advancements in Generative Adversarial Networks for Computer Visioncited by 0
  9. 2024Kolmogorov-Smirnov GANcited by 0
  10. 2025The informativeness of the gradient revisitedcited by 0