A game-theoretic approach for Generative Adversarial Networks
This addresses the bottleneck of hard training in GANs, offering a method to improve performance, but it appears incremental as it builds on existing game-theoretic approaches.
The paper tackles the training difficulty of Generative Adversarial Networks (GANs) by proposing a stochastic relaxed forward-backward algorithm based on a game-theoretic formulation, proving convergence to an exact or approximate solution under monotonicity conditions.
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.