  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, as demonstrated through extensive quantitative
and qualitative evaluation. 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. Our code is publicly available at
https://github.com/rmokady/mbu-content-tansfer.
