Papers › One-Sided Unsupervised Domain Mapping
One-Sided Unsupervised Domain Mapping
Sagie Benaim, Lior Wolf
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 https://github.com/sagiebenaim/DistanceGAN .
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