September 12, 2018
NAM: Non-Adversarial Unsupervised Domain Mapping
European Conference on Computer Vision (ECCV)
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 cross-domain mapping task.
By: Yedid Hoshen, Lior Wolf
Facebook AI Research