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Oral
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Yunchen Pu · Shuyang Dai · Zhe Gan · Weiyao Wang · Guoyin Wang · Yizhe Zhang · Ricardo Henao · Lawrence Carin
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.
Author Information
Yunchen Pu (Duke)
Shuyang Dai (Duke University)
Zhe Gan (Duke University)
Weiyao Wang (Duke)
Guoyin Wang (Duke University)
Yizhe Zhang (Duke University)
Ricardo Henao (Duke University)
Lawrence Carin (Duke)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Poster: JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets »
Fri. Jul 13th 04:15 -- 07:00 PM Room Hall B #109
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