Title: One-Sided Unsupervised Domain Mapping,
Abstract: In unsupervised domain mapping, the learner is given two unmatched datasets
$A$ and $B$. The goal is to learn a mapping $G_{AB}$ that translates a sample
in $A$ to the analog sample in $B$. Recent approaches have shown that when
learning simultaneously both $G_{AB}$ and the inverse mapping $G_{BA}$,
convincing mappings are obtained. In this work, we present a method of learning
$G_{AB}$ without learning $G_{BA}$. This is done by learning a mapping that
maintains the distance between a pair of samples. Moreover, good mappings are
obtained, even by maintaining the distance between different parts of the same
sample before and after mapping. We present experimental results that the new
method not only allows for one sided mapping learning, but also leads to
preferable numerical results over the existing circularity-based constraint.
Our entire code is made publicly available at
this https URL .