Review for NeurIPS paper: The Origins and Prevalence of Texture Bias in Convolutional Neural Networks

The paper is very well written, seemingly involves a massive amount of wordload, and answers most of the questions clearly with evidence and offer conjectures of the unanswerable questions to guide future research. Despite the high quality, I noticed several drawbacks and suggest the authors to address them. In the abstract, the paper says the differences "arise not from differences in their internal workings, but from differences in the data that they see", which seems to suggest that whether the model learns texture or shape primarily depends on the data seen, yet in the experiments, the authors demonstrate that, with more carefully designed regularizations (termed as "self-supervised losses" in the paper), the model can be pushed to focus more on the shape. This empirical observation seems to contradict with the main claim in the abstract since I suppose losses are one of the "internal workings" (or what does "internal workings" mean exactly?). I suggest the authors to revise corresponding texts to reflect this more accurately.