Towards Principled Methods for Training Generative Adversarial Networks

International Conference on Learning Representations (ICLR) 2017


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

Related Publications

All Publications

NeurIPS - December 7, 2020

Labelling unlabelled videos from scratch with multi-modal self-supervision

Yuki M. Asano, Mandela Patrick, Christian Rupprecht, Andrea Vedaldi

NeurIPS - December 7, 2020

Adversarial Example Games

Avishek Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton

NeurIPS - December 7, 2020

Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search

Linnan Wang, Rodrigo Fonseca, Yuandong Tian

NeurIPS - December 7, 2020

Joint Policy Search for Multi-agent Collaboration with Imperfect Information

Yuandong Tian, Qucheng Gong, Tina Jiang

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy