One-Sided Unsupervised Domain Mapping
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.
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.
The paper is well structured and easy to follow. The main contribution is the introduction of a distance constraint to recent state-of-the-art unsupervised domain adaptation pipeline. The introduction of the distance constraint and the assumption held by this constraint, i.e. there is a high degree of correlation between the pairwise distances in the 2 domains, are well motivated with experimental evidence.
Although literature review is quite extensive, [a-c] might be relevant to discuss. 
[a] https://arxiv.org/pdf/1608.06019.pdf 
[b] https://arxiv.org/pdf/1612.05424.pdf
[c] https://arxiv.org/pdf/1612.02649.pdf
The influence of having a distance loss is extensively evaluated on a variety of datasets for models based on DiscoGAN, by changing the loss function (either maintaining all its components, or switching off some of them).  Qualitative and quantitative results show the potential of the distance loss, both in combination with the cycle loss and on its own, highlighting the possibility of learning only one-sided systems. However, given that the quantitative evaluation pipeline is not robust (i.e. it depends on training a regressor), it is difficult to make any strong claims on the performance of the method.
The authors qualitatively and quantitatively assess their contributions for models based on CycleGAN as well. Quantitative results are showed for SVHN to MNIST mapping. However, among the large variety of mappings shown in the CycleGAN paper, authors only pick the horse to zebra mapping to make the qualitative comparison. Given the subjectivity of this kind of comparison, it would be more compelling if the authors could show some other mappings such as season transfer, style transfer, other object transfigurations or labels2photo/photo2label task. Quantitative evaluation of CycleGAN-based models could be further improved by following the FCN-scores of the CycleGAN paper on the task labels/photo or photo/labels.
Finally, in the conclusions, the claim “which empirically outperforms the circularity loss” (lines 293-294) seems a bit too strong given the experimental evidence.