Triangle generative adversarial networks

Published

Conference Paper

© 2017 Neural information processing systems foundation. All rights reserved. A Triangle Generative Adversarial Network (Δ-GAN) is developed for semi-supervised cross-domain joint distribution matching, where the training data consists of samples from each domain, and supervision of domain correspondence is provided by only a few paired samples. Δ-GAN consists of four neural networks, two generators and two discriminators. The generators are designed to learn the two-way conditional distributions between the two domains, while the discriminators implicitly define a ternary discriminative function, which is trained to distinguish real data pairs and two kinds of fake data pairs. The generators and discriminators are trained together using adversarial learning. Under mild assumptions, in theory the joint distributions characterized by the two generators concentrate to the data distribution. In experiments, three different kinds of domain pairs are considered, image-label, image-image and image-attribute pairs. Experiments on semi-supervised image classification, image-to-image translation and attribute-based image generation demonstrate the superiority of the proposed approach.

Duke Authors

Cited Authors

  • Gan, Z; Chen, L; Wang, W; Pu, Y; Zhang, Y; Liu, H; Li, C; Carin, L

Published Date

  • January 1, 2017

Published In

Volume / Issue

  • 2017-December /

Start / End Page

  • 5248 - 5257

International Standard Serial Number (ISSN)

  • 1049-5258

Citation Source

  • Scopus