Learning Visual Representations for Transfer Learning by Suppressing Texture

Understand

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.