Before revision:  in general, models learn shape at least as easily as  texture . Moreover, although ImageNet training leads to classifier weights that classify ambiguous images according to texture, shape is decodable from the hidden representations of ImageNet networks. Turning to the question of the origin of texture bias , we identify consistent effects of task, architecture, preprocessing, and hyperparameters. Different self-supervised training objectives and different architectures have  significant and largely independent effects on the shape bias of the learned representations.
After revision:  in general, models learn shape at least as easily as texture bias in CNNs trained on ImageNet? Different unsupervised training objectives and different architectures have significant and largely independent effects on the shape bias of the learned representations.