SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer

arXiv:2301.12811v4 Announce Type: replace
Abstract: Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives. This paper addresses the question of whether such optimization actually provides the generator with gradients that make its distribution close to the target distribution. We derive metrizable conditions, sufficient conditions for the discriminator to serve as the distance between the distributions by connecting the GAN formulation with the concept of sliced optimal transport. Furthermore, by leveraging …

arxiv cs.lg gan layer linear type