From: NeurIPS.cc/2021/Workshop/DistShift/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: Propose another logit based OOD score using Fisher-Rao distance between probabilities. Experiments show that it works better than Mahalanobis on some far OOD scenarios especially when using OOD data as the validation set. Reviewers note that the clarity is good, experiments are well conducted, and using the FR distance is interesting to try.

Like the original Mahalabonis paper, the advertisement in the abstract is misleading "without using OOD samples" - although at least in Table 1, OOD samples are clearly used for validation.  In the actual OOD-oblivious table 1 right, Igeood does not do uniformly better than Mahalanobis, particularly for the ResNet. However, the authors write "IGEOOD record improvements up to
133 10.3%, and improves by 3% the average TNR at TPR-95% across all datasets and models." in the comparison paragraph. We expect that the authors seriously reformulate their contributions and improvements.
Further, there are other baselines (e.g. Gram) that are now doing better than Mahalanobis in some cases - why isn't there a comparison with these? As suggested by the reviewers, please also add error bars and address additional comments by the reviewers in the final version of the manuscript.