  There has been considerable recent interest in interpretable concept-based
models such as Concept Bottleneck Models (CBMs), which first predict
human-interpretable concepts and then map them to output classes. To reduce
reliance on human-annotated concepts, recent works have converted pretrained
black-box models into interpretable CBMs post-hoc. However, these approaches
predefine a set of concepts, assuming which concepts a black-box model encodes
in its representations. In this work, we eliminate this assumption by
leveraging unsupervised concept discovery to automatically extract concepts
without human annotations or a predefined set of concepts. We further introduce
an input-dependent concept selection mechanism that ensures only a small subset
of concepts is used across all classes. We show that our approach improves
downstream performance and narrows the performance gap to black-box models,
while using significantly fewer concepts in the classification. Finally, we
demonstrate how large vision-language models can intervene on the final model
weights to correct model errors.
