Reviews: One-Sided Unsupervised Domain Mapping

This paper tackles the problem of unsupervised domain adaptation. The paper introduces a new constraint, which compares samples and enforces high cross-domain correlation between the matching distances computed in each domain. An alternative to pairwise distance is provided, for cases in which we only have access to one data sample at a time. In this case, the same rationale can be applied by splitting the images and comparing the distances between their left/right or up/down halves in both domains. The final unsupervised domain adaptation model is trained by combining previously introduced losses (adversarial loss and circularity loss) with the new distance loss, showing that the new constraint is effective and allows for one directional mapping.