Review for NeurIPS paper: Further Analysis of Outlier Detection with Deep Generative Models

The paper investigates out-of-distribution behavior of deep generative models, specifically the counter intuitive results reported in prior work where deep generative models were shown to assign higher likelihood to out-of-distribution inputs. The authors propose a new white noise test (WN test), theoretically motivate the proposed test and show that it outperforms likelihood and likelihood ratios. The reviewers raised concerns about experimental setup (other datasets and models), WN assumption and connections to other related methods such as typicality test. This was a borderline paper. During the discussion, majority of the reviewers agreed that the author rebuttal addresses their major concerns except for R2.