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 based on haphazard loss functions. The two time-scale update rule has separate learning rates for the generator and the discriminator. With the aid of the stochastic approximation theory, this paper demonstrates that the TTUR reaches a point of convergence under the…