# An Online Learning Approach to Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1706.03269 · HTML: https://mlsanity.com/paper/1706.03269/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2017.

## Built on by (most cited first, 13 of 13)

- [Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization](https://mlsanity.com/paper/2408.11974/) (2024)
- [A Multi-Armed Bandit Approach to Online Selection and Evaluation of Generative Models](https://mlsanity.com/paper/2406.07451/) (2024)
- [Provably convergent quasistatic dynamics for mean-field two-player zero-sum games](https://mlsanity.com/paper/2202.10947/) (2022)
- [Direct-Search for a Class of Stochastic Min-Max Problems](https://mlsanity.com/paper/2102.11386/) (2021)
- [Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach](https://mlsanity.com/paper/2104.04886/) (2021)
- [Game of GANs: Game-Theoretical Models for Generative Adversarial Networks](https://mlsanity.com/paper/2106.06976/) (2021)
- [Generative Minimization Networks: Training GANs Without Competition](https://mlsanity.com/paper/2103.12685/) (2021)
- [Randomized Stochastic Gradient Descent Ascent](https://mlsanity.com/paper/2111.13162/) (2021)
- [PromptWise: Online Learning for Cost-Aware Prompt Assignment in Generative Models](https://mlsanity.com/paper/2505.18901/) (2025)
- [PAPAL: A Provable PArticle-based Primal-Dual ALgorithm for Mixed Nash Equilibrium](https://mlsanity.com/paper/2303.00970/) (2023)
- [Competitive Gradient Optimization](https://mlsanity.com/paper/2205.14232/) (2022)
- [Nested Annealed Training Scheme for Generative Adversarial Networks](https://mlsanity.com/paper/2501.11318/) (2025)
- [FraudFox: Adaptable Fraud Detection in the Real World](https://mlsanity.com/paper/2603.13014/) (2026)

## Abstract (arXiv)

We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be difficult to train due to instabilities caused by a difficult minimax optimization problem. In this paper, we view the problem of training GANs as finding a mixed strategy in a zero-sum game. Building on ideas from online learning we propose a novel training method named Chekhov GAN 1 . On the theory side, we show that our method provably converges to an equilibrium for semi-shallow GAN architectures, i.e. architectures where the discriminator is a one layer network and the generator is arbitrary. On the practical side, we develop an efficient heuristic guided by our theoretical results, which we apply to commonly used deep GAN architectures. On several real world tasks our approach exhibits improved stability and performance compared to standard GAN training.