Towards Principled Methods for Training Generative Adversarial Networks

TL;DR

This paper aims to develop a theoretical understanding of GAN training dynamics, addressing issues like instability and saturation, and proposing grounded solutions supported by experiments.

Contribution

It provides a rigorous theoretical analysis of GAN training problems and introduces new tools and directions for improving training stability.

Findings

Identified key problems in GAN training such as instability and saturation.

Provided theoretical proofs and analysis of these issues.

Suggested practical solutions grounded in theory.

Abstract

The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks. In order to substantiate our theoretical analysis, we perform targeted experiments to verify our assumptions, illustrate our claims, and quantify the phenomena. This paper is divided into three sections. The first section introduces the problem at hand. The second section is dedicated to studying and proving rigorously the problems including instability and saturation that arize when training generative adversarial networks. The third section examines a practical and theoretically grounded direction towards solving these problems, while introducing new tools to study them.