Recent work has indicated that, unlike humans, ImageNet-trained CNNs tend to classify images by texture rather than by shape. How pervasive is this bias, and where does it come from? We find that, when trained on datasets of images with conflicting shape and texture, CNNs learn to classify by shape at least as easily as by texture. What factors, then, produce the texture bias in CNNs trained on ImageNet? Different unsupervised training objectives and different architectures have small but significant and largely independent effects on the level of texture bias. However, all objectives and architectures still lead to models that make texture-based classification decisions a majority of the time, even if shape information is decodable from their hidden representations. The effect of data augmentation is much larger. By taking less aggressive random crops at training time and applying simple, naturalistic augmentation (color distortion, noise, and blur), we train models that classify ambiguous images by shape a majority of the time, and outperform baselines on out-of-distribution test sets. Our results indicate that apparent differences in the way humans and ImageNet-trained CNNs process images may arise not primarily from differences in their internal workings, but from differences in the data that they see.

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.

This paper studies an important question and the reviewers found the results convincing enough about the main claim of data augmentation mechanisms contributing to texture bias.

Recent work has indicated that, unlike humans, ImageNet-trained CNNs tend to classify images by texture rather than by shape. How pervasive is this bias, and where does it come from? We find that, when trained on datasets of images with conflicting shape and texture, CNNs learn to classify by shape at least as easily as by texture. What factors, then, produce the texture bias in CNNs trained on ImageNet? Different unsupervised training objectives and different architectures have small but significant and largely independent effects on the level of texture bias.