Towards Principled Methods for Training Generative Adversarial Networks

Understand

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.