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Recent work has indicated that, unlike humans, ImageNet-trained CNNs tend toclassify 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 imageswith conflicting shape and texture, CNNs learn to classify by shape at least aseasily as by texture. What factors, then, produce the texture bias in CNNstrained on ImageNet? Different unsupervised training objectives and differentarchitectures have small but significant and largely independent effects on thelevel of texture bias. However, all objectives and architectures still lead tomodels that make texture-based classification decisions a majority of the time,even if shape information is decodable from their hidden representations. Theeffect of data augmentation is much larger. By taking less aggressive randomcrops at training time and applying simple, naturalistic augmentation (colordistortion, noise, and blur), we train models that classify ambiguous images byshape a majority of the time, and outperform baselines on out-of-distributiontest sets. Our results indicate that apparent differences in the way humans andImageNet-trained CNNs process images may arise not primarily from differencesin their internal workings, but from differences in the data that they see.

The Origins and Prevalence of Texture Bias in Convolutional Neural Networks