the paper being "focused and well-written " (R3), having " contributions which are relevant (for a part of) the NeurIPS

Dear reviewers, thank you for your time to thoroughly read and review our paper. Y ou said "the addressed problem is relevant and timely" (R4), "useful in practice with many applications" (R1), with Quantifying for the first time the gap between train and test losses in the approach of Ballé et al. (see below) The proposed approach only marginally improves PSNR for hyperprior models. Note that in compression lightweight models are very relevant in practice . Secondly, our empirical results allow us for the first time to quantify the gap between training and test losses . Finally, universal quantization has the potential to lead to much bigger improvements in the future .