One-Sided Unsupervised Domain Mapping

Understand

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 $G_{BA}$.