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

Three knowledgeable referees support acceptance, and I also recommend acceptance. The key contribution of this submission is a new reconstruction loss for VAEs (somewhat like JPEG loss) that matches human perception more closely than traditional VAE reconstruction losses (e.g. For applications where the goal is to generate sharp images rather than to maximize the likelihood of held-out data, the proposed method is a good alternative to other known ways of generating sharp images with VAEs (i.e, autoregressive/flow-based decoders and adversarial loss function). Unlike these alternatives, the proposed method introduces few additional parameters to learn from the data. R1's and R2's concern about the lack of quantitative measures of performance is justified, but the author response also makes a compelling point about the difficulty of picking a fair quantitative metric.