  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.
