Triangle Generative Adversarial Networks
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Triangle Generative Adversarial Networks
A Triangle Generative Adversarial Network ($Delta$GAN) is developed for semisupervised crossdomain 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 twoway 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, imagelabel, imageimage and imageattribute pairs. Experiments on semisupervised image classification, imagetoimage translation and attributebased image generation demonstrate the superiority of the proposed approach.
Triangle Generative Adversarial Networks
by Zhe Gan, Liqun Chen, Weiyao Wang, Yunchen Pu, Yizhe Zhang, Hao Liu, Chunyuan Li, Lawrence Carin
https://arxiv.org/pdf/1709.06548v1.pdf
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