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GANs Trained by a Two Time-Scale Update Rule ... - NeurIPS

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler Sepp Hochreiter LIT AI Lab & Institute of Bioinformatics, Johannes Kepler University Linz A-4040 Linz, Austria Abstract generative adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the con- vergence of GAN training has still not been proved. We propose a two Time-Scale Update rule (TTUR) for training GANs with stochastic gradient descent on ar- bitrary GAN loss functions.

Generative Adversarial Networks (GANs) excel at creating realistic images with ... discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for ... gorithms based on ...

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