  Unsupervised image-to-image translation is a class of computer vision
problems which aims at modeling conditional distribution of images in the
target domain, given a set of unpaired images in the source and target domains.
An image in the source domain might have multiple representations in the target
domain. Therefore, ambiguity in modeling of the conditional distribution
arises, specially when the images in the source and target domains come from
different modalities. Current approaches mostly rely on simplifying assumptions
to map both domains into a shared-latent space. Consequently, they are only
able to model the domain-invariant information between the two modalities.
These approaches usually fail to model domain-specific information which has no
representation in the target domain. In this work, we propose an unsupervised
image-to-image translation framework which maximizes a domain-specific
variational information bound and learns the target domain-invariant
representation of the two domain. The proposed framework makes it possible to
map a single source image into multiple images in the target domain, utilizing
several target domain-specific codes sampled randomly from the prior
distribution, or extracted from reference images.
