Reviews: Reconstruct & Crush Network

The paper proposes a classification technique using deep nets to deal with: (a) covariate shift (i.e., when the train and test data do not share the same distribution) (b) PU settings (i.e., when there are only positive and unlabeled datapoints are available). The key idea is to train an auto-encoder that reconstruct the positive instances with small error and the remaining instances (negative or unlabeled) with a large error (above a desired threshold). This structure forces the network to learn patterns that are intrinsic to the positive class (as opposed to features that are discriminative across different classes). The experiments highlight that the proposed method outperforms baselines across different tasks with different data types (image, short text, and dialogues). Overall, I enjoyed reading the paper.