Transcription of UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION
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UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION
Under review as a conference paper at ICLR 2017 UNSUPERVISEDCROSS-DOMAINIMAGEGENERATIONY aniv Taigman, Adam Polyak & Lior WolfFacebook AI ResearchTel-Aviv, study the problem of transferring a sample in one domain to an analog samplein another domain . Given two related domains,SandT, we would like to learn agenerative functionGthat maps an input sample fromSto the domainT, such thatthe output of a given functionf, which accepts inputs in either domains, wouldremain unchanged. Other than the functionf, the training data is unsupervisedand consist of a set of samples from each domain Transfer Network (DTN) we present employs a compound loss func-tion that includes a multiclass GAN loss, anf-constancy component, and a regu-larizing component that encouragesGto map samples fromTto themselves.
Under review as a conference paper at ICLR 2017 UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION Yaniv Taigman, Adam Polyak & Lior Wolf Facebook AI Research Tel-Aviv, Israel fyaniv,adampolyak,wolfg@fb.com
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