Triangle Generative Adversarial Networks

Abstract

A Triangle Generative Adversarial Network ($\Delta$-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. $\Delta$-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.

To appear in NIPS 2017

References in corpus (16)

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Cited by in corpus (6)

• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
• Spatial Evolutionary Generative Adversarial Networks
• Image Captioning with Very Scarce Supervised Data: Adversarial Semi-Supervised Learning Approach
• Adversarial Symmetric Variational Autoencoder
• Symmetric Variational Autoencoder and Connections to Adversarial Learning
• Joint Learning of Generative Translator and Classifier for Visually Similar Classes