Review for NeurIPS paper: A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model

Weaknesses: I have one critical concern with this paper, which is that the proposed model presented here is extremely similar to one result from "A General and Adaptive Robust Loss Function", Jonathan T. Barron, CVPR, 2019. Section 3.1 of that paper (going from the arxiv version) has results on improving reconstruction/sampling quality from VAEs by using a loss on DCT coefficients of YUV images, very similar to what is done here. They also propose a loss with a heavy-tailed distribution that looks a lot like Equation 8 of this submission, and present a method where they optimize over the scale of the loss being imposed on each coefficient of the DCT (similar to this submission). And the improvement in sample/reconstruction quality they demonstrate looks a lot like what is shown in this submission. Given these overwhelming similarities, I'm unable to support the acceptance of this paper without a comparison to the approach presented in that work.