NeurIPS 2020

HRN: A Holistic Approach to One Class Learning

Meta Review

This paper proposes a novel deep one-class classification method where a regularization technique is specially designed for one-class classification problem. It also provides insights on the bottlenecks of previous methods for this problem; one insight is quite novel and has not been considered yet (representation learning from one-class data is biased to the given training data), since previous methods mainly focused on the other (deep network outputs become over-confident given one-class data). I am feeling the paper may further inspire more cleverly designed methods for this problem! While in the beginning the reviewers had some concerns (mainly the clarity and the generality that is related to the significance), the authors did a particularly good job in their rebuttal (showing that the proposal is not limited to a single surrogate loss function). Thus in the end, all of us have agreed to accept this paper for publication! Please carefully address the concerns from all 3 reviewers in the next version.