Semi-Supervised Learning with Generative Adversarial Networks

Venue

arXiv:1606.01583 [cs, stat], vol.

Publication Year

2016

Keywords

Statistics - Machine Learning,Computer Science - Machine Learning

Authors

  • Augustus Odena

Abstract

We extend Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels. We train a generative model G and a discriminator D on a dataset with inputs belonging to one of N classes. At training time, D is made to predict which of N+1 classes the input belongs to, where an extra class is added to correspond to the outputs of G. We show that this method can be used to create a more data-efficient classifier and that it allows for generating higher quality samples than a regular GAN.