  This paper studies the unsupervised cross-domain translation problem by
proposing a generative framework, in which the probability distribution of each
domain is represented by a generative cooperative network that consists of an
energy-based model and a latent variable model. The use of generative
cooperative network enables maximum likelihood learning of the domain model by
MCMC teaching, where the energy-based model seeks to fit the data distribution
of domain and distills its knowledge to the latent variable model via MCMC.
Specifically, in the MCMC teaching process, the latent variable model
parameterized by an encoder-decoder maps examples from the source domain to the
target domain, while the energy-based model further refines the mapped results
by Langevin revision such that the revised results match to the examples in the
target domain in terms of the statistical properties, which are defined by the
learned energy function. For the purpose of building up a correspondence
between two unpaired domains, the proposed framework simultaneously learns a
pair of cooperative networks with cycle consistency, accounting for a two-way
translation between two domains, by alternating MCMC teaching. Experiments show
that the proposed framework is useful for unsupervised image-to-image
translation and unpaired image sequence translation.
