Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer

Understand

We study the problem of learning to map, in an unsupervised way, between domains A and B, such that the samples b in B contain all the information that exists in samples a in A and some additional information.

• For example, ignoring occlusions, B can be people with glasses, A people without, and the glasses, would be the added information.
• When mapping a sample a from the first domain to the other domain, the missing information is replicated from an independent reference sample b in B.
• Thus, in the above example, we can create, for every person without glasses a version with the glasses observed in any face image.