Review for NeurIPS paper: ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping

This paper proposes a model for simultaneous classification and feature attribution in the context of medical image classification. The model uses GAN to learn two representations from pairs (x, y) of input images of different classes. One representation is class-relevant (z a, a for attribution) and the other is class-irrelevant (z c, c for content). The class-relevant representation is used for classification. Both representations are fed to a generator G to synthesize images so as to achieve domain translation.