Explorations in Texture Learning

In this work, we investigate 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. Code is available at https://github.com/blainehoak/ Convolutional Neural Networks (CNNs) have been shown to be more biased towards texture (repeated patterns), rather than shape like human vision is Geirhos et al. (2019).