NeurIPS 2020

Identifying Mislabeled Data using the Area Under the Margin Ranking

Meta Review

This paper proposes a simple strategy for identifying training samples that may be mislabeled. The paper received mixed reviews from the reviewers, with one reviewer in particular strongly arguing that simple and effective methods are deserving of publication, while other reviewers were concerned that the approach is too straightforward. Weighing these arguments, it was felt in discussion that this paper could be of interest as a straightforward approach to deal with the important problem of label noise.