Ron Mokady | Mask Based Unsupervised Content Transfer
Details
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Abstract:
We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other. The proposed method disentangles the common and separate parts of these domains and, through the generation of a mask, focuses the attention of the underlying network to the desired augmentation alone, without wastefully reconstructing the entire target. This enables state-of-the-art quality and variety of content translation. Our method is also capable of adding the separate content of different guide images and domains as well as remove existing separate content. Furthermore, our method enables weakly-supervised semantic segmentation of the separate part of each domain, where only class labels are provided.
Bio:
Ron Mokady is a PhD student at Tel-Aviv university under the supervision of Prof. Daniel Cohen-Or and Dr. Amit Bermano. Interested in Computer Vision, Computer Graphics and Deep Learning. Currently research intern at Facebook AI.