From: NeurIPS.cc/2021/Workshop/DistShift/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: The paper analyzes empirically the biases of FID, a metric commonly used to measure the distance between distributions, e.g. source distribution and the distribution of images generated by a generative model. The authors use the corruptions in ImageNet-C to identify possible biases of FID, but, as noted by Reviewer H4Tp, this methodology is vulnerable to miscalibrated corruption strengths (i.e. one corruption being stronger than another at all corruption levels). Nevertheless, the reviewers agree that the preliminary results are interesting and that the paper explains clearly the experimental methodology. Therefore, we accept the paper and encourage the authors to address the feedback from the reviewers.