אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given function f, which accepts inputs in either domains, would remain unchanged. Other than the function f, the training data is unsupervised and consist of a set of samples from each domain. The Domain Transfer Network (DTN) we present employs a compound loss function that includes a multiclass GAN loss, an f-constancy component, and a regularizing component that encourages G to map samples from T to themselves. We apply our method to visual domains including digits and face images and demonstrate its ability to generate convincing novel images of previously unseen entities, while preserving their identity.

Bio:
Adam Polyak is a research engineer at Facebook AI Research(FAIR) in Facebook Tel-Aviv, where he works on developing and understanding systems with human level intelligence. Before joining FAIR, Adam completed his M.Sc in computer science at Tel-Aviv University, under the supervision of Prof. Lior Wolf. His thesis focused on methods to accelerate and compress neural networks to allow their deployment on devices with limited computational power.​