JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets

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Authors Yizhe Zhang, Ricardo Henao, Lawrence Carin, Shuyang Dai, Guoyin Wang, Weiyao Wang, Yunchen Pu, Zhe Gan
Journal/Conference Name ICML 2018 7
Paper Category
Paper Abstract A new generative adversarial network is developed for joint distribution matching. Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains). This is achieved by learning to sample from conditional distributions between the domains, while simultaneously learning to sample from the marginals of each individual domain. The proposed framework consists of multiple generators and a single softmax-based critic, all jointly trained via adversarial learning. From a simple noise source, the proposed framework allows synthesis of draws from the marginals, conditional draws given observations from a subset of random variables, or complete draws from the full joint distribution. Most examples considered are for joint analysis of two domains, with examples for three domains also presented.
Date of publication 2018
Code Programming Language Multiple
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