Tutorial on Theory and Application of Generative Adversarial Networks

Event: CVPR 2017 at Honolulu

Date: Wednesday, 7/26/2017

Organizers: Ming-Yu Liu, Julie Bernauer, Jan Kautz

Time: PM 13:30 --- 14:30

Description

Generative adversarial network (GAN) has recently emerged as a promising generative modeling approach. It consists of a generative network and a discriminative network. Through the competition between the two networks, it learns to model the data distribution. In addition to modeling the image/video distribution in computer vision problems, the framework finds use in defining visual concept using examples. To a large extent, it eliminates the need of hand-crafting objective functions for various computer vision problems. In this tutorial, we will present an overview of generative adversarial network research. We will cover several recent theoretical studies as well as training techniques and will also cover several vision applications of generative adversarial networks.

Outlines

slides

Part 1:

1. Introduction

2. GAN Theory

3. GAN Training

Part 2:

4. Joint image distribution and video distribution learning

5. Applications with focus on image translation