Abstract. Several methods were recently proposed for the task of translating images between domains without prior knowledge in the form of
correspondences. The existing methods apply adversarial learning to ensure that the distribution of the mapped source domain is indistinguishable from the target domain, which suffers from known stability issues.
In addition, most methods rely heavily on “cycle” relationships between
the domains, which enforce a one-to-one mapping. In this work, we introduce an alternative method: Non-Adversarial Mapping (NAM), which
separates the task of target domain generative modeling from the crossdomain mapping task. NAM relies on a pre-trained generative model of
the target domain, and aligns each source image with an image synthesized from the target domain, while jointly optimizing the domain mapping function. It has several key advantages: higher quality and resolution
image translations, simpler and more stable training and reusable target
models. Extensive experiments are presented validating the advantages
of our method