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