Transcription of UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION
{{id}} {{{paragraph}}}
Example: air traffic controller
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.
can assume that if such methods were appropriate for emoji synthesis, automatic face emoji services would be available. Unsupervised domain adaptation addresses the following problem: given a labeled training set in S Y, for some target space Y, and an unlabeled set of samples from domain T, learn a …
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: