Reviews: Learning Latent Subspaces in Variational Autoencoders

Updated (due to rebuttal & discussion w/ R2): The authors reiterate in their rebuttal their core contributions of "extracting information beyond binary labels" and "attribute manipulation from a single image", together with the promise to clarify it in the paper. The contributions are relevant to the community, since this form of hierarchical disentangling seems novel. That said, there is some degree of similarity of the proposed variational approach to IGN (Deep Convolutional Inverse Graphics Network https://arxiv.org/abs/1503.03167). IGN is cited, but not discussed in detail, and an empirical comparison is not provided, despite being applicable to the current setting as well. Nevertheless, since the selling point of the paper seems to be the ability to discover sub-categories from only category labels, which is not addressed in IGN and is an interesting empirical find, I increased my score to be marginally above the acceptance threshold.