  Recent literature has shown that features obtained from supervised training
of CNNs may over-emphasize texture rather than encoding high-level information.
In self-supervised learning in particular, texture as a low-level cue may
provide shortcuts that prevent the network from learning higher level
representations. To address these problems we propose to use classic methods
based on anisotropic diffusion to augment training using images with suppressed
texture. This simple method helps retain important edge information and
suppress texture at the same time. We empirically show that our method achieves
state-of-the-art results on object detection and image classification with
eight diverse datasets in either supervised or self-supervised learning tasks
such as MoCoV2 and Jigsaw. Our method is particularly effective for transfer
learning tasks and we observed improved performance on five standard transfer
learning datasets. The large improvements (up to 11.49\%) on the
Sketch-ImageNet dataset, DTD dataset and additional visual analyses with
saliency maps suggest that our approach helps in learning better
representations that better transfer.
