Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
The paper proposes progressive augmentation for GANs and shows that it leads to stable training and improves FID consistently. The author response addressed some of the initial concerns, and all the reviewers lean towards accepting the paper. It's nice to see that the proposed technique appears to be complementary to other regularization schemes, so it has the potential to be more widely useful for other machine learning problems (the authors themselves mention this as one of the future directions). I encourage the authors to incorporate reviewer feedback into the final version.