Explorations in Texture Learning

Blaine Hoak; Patrick McDaniel

Explorations in Texture Learning

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arxiv 2403.09543 v1 pith:N554UXUZ submitted 2024-03-14 cs.CV cs.LG

Blaine Hoak , Patrick McDaniel This is my paper · ORCID

classification cs.CVcs.LG

keywords textureexpectedlearningassociationsclassesobjectstrongtextures

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In this work, we investigate \textit{texture learning}: the identification of textures learned by object classification models, and the extent to which they rely on these textures. We build texture-object associations that uncover new insights about the relationships between texture and object classes in CNNs and find three classes of results: associations that are strong and expected, strong and not expected, and expected but not present. Our analysis demonstrates that investigations in texture learning enable new methods for interpretability and have the potential to uncover unexpected biases.

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Err on the Side of Texture: Texture Bias on Real Data
cs.CV 2024-12 conditional novelty 6.0 of 10

A new metric and texture-identification method show that ImageNet classifiers rely heavily on specific textures, but the claim that texture bias explains natural adversarial examples is largely a consequence of how te...

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