  Generative adversarial networks (GAN) are a widely used class of deep
generative models, but their minimax training dynamics are not understood very
well. In this work, we show that GANs with a 2-layer infinite-width generator
and a 2-layer finite-width discriminator trained with stochastic gradient
ascent-descent have no spurious stationary points. We then show that when the
width of the generator is finite but wide, there are no spurious stationary
points within a ball whose radius becomes arbitrarily large (to cover the
entire parameter space) as the width goes to infinity.
