GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

When it comes to the formation of real-looking images using some complex models, Generative Adversarial Networks do not disappoint. The complex models involved are often the types with infeasible maximum likelihoods. Be that as it may, there is not yet any proof for the convergence of GANs training. This paper proposes a TTUR (a two-time scale update rule) for training the Generative Adversarial Networks with a descent of stochastic gradient...