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%2C which first predict human-interpretable concepts and then map them to output classes. To reduce reliance on human-annotated concepts%2C recent works have converted pretrained black-box models into interpretable CBMs post-hoc. However%2C these approaches predefine a set of concepts%2C assuming which concepts a black-box model encodes in its representations. In this work%2C 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%2C while using significantly fewer concepts in the classification. Finally%2C we demonstrate how large vision-language models can intervene on the final model weights to correct model errors.?quality=80&w=800)
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