Understanding GANs via Generalization Analysis for Disconnected Support

Masaaki Imaizumi, Kenji Fukumizu

Abstract: This paper provides theoretical analysis of generative adversarial networks (GANs) to explain its advantages over other standard methods of learning probability measures. GANs learn a probability through observations, using the objective function with a generator and a discriminator. While many empirical results indicate that GANs can generate realistic samples, the reason for such successful performance remains unelucidated. This paper focuses the situation where the target probability measure satisfies the disconnected support property, which means a separate support of a probability, and relates it with the advantage of GANs. It is theoretically shown that, unlike other popular models, GANs do not suffer from the decrease of generalization performance caused by the disconnected support property. We rigorously quantify the generalization performance of GANs of a given architecture, and compare it with the performance of the other models. Based on the theory, we also provide a guideline for selecting deep network architecture for GANs. We demonstrate some numerical examples which support our results.

Keywords: Generalization analysis, Statistical estimation, Understanding GANs, Disconnected support

TL;DR: We investigate the generalization performance of GANs and show how GANs outperform others with a specific property of data.