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.

开发了一种新的生成对抗网络，用于联合分布匹配，并能够从多个随机变量中学习联合分布，同时学习采样每个单独领域的边缘分布和领域间的条件分布，该框架由多个生成器和一个基于Softmax的评论家组成，并通过对抗性学习进行联合训练。

JointGAN: 用生成式对抗网络实现多领域联合分布学习