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

Understand

Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible.

• However, the convergence 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 arbitrary GAN loss functions.
• TTUR has an individual learning rate for both the discriminator and the generator.