From: NeurIPS.cc/2021/Workshop/DistShift/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: The paper proposes to train GANs in a sequence (with the generated data from one GAN being the training data for the next and so on). The authors then assess the characteristics of images produced by the GANs in the sequence and observe that the distribution shift becomes more severe towards the end of the sequence. The reviewers agree that the approach is interesting and novel and the experimental results intriguing, therefore we think the paper would be a good addition to the workshop. However, the paper could benefit from providing concrete examples of situations where this kind of analysis could be useful.