Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation
Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation
While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we …