# A game-theoretic approach for Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2003.13637 · HTML: https://mlsanity.com/paper/2003.13637/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games](https://mlsanity.com/paper/2109.01795/) (2021)

## Abstract (arXiv)

Generative adversarial networks (GANs) are a class of generative models, known for producing accurate samples. The key feature of GANs is that there are two antagonistic neural networks: the generator and the discriminator. The main bottleneck for their implementation is that the neural networks are very hard to train. One way to improve their performance is to design reliable algorithms for the adversarial process. Since the training can be cast as a stochastic Nash equilibrium problem, we rewrite it as a variational inequality and introduce an algorithm to compute an approximate solution. Specifically, we propose a stochastic relaxed forward-backward algorithm for GANs. We prove that when the pseudogradient mapping of the game is monotone, we have convergence to an exact solution or in a neighbourhood of it.