Concept Bottleneck Models Without Predefined Concepts

• URL: http://arxiv.org/abs/2407.03921v1
• Date: Thu, 4 Jul 2024 13:34:50 GMT
• Title: Concept Bottleneck Models Without Predefined Concepts
• Authors: Simon Schrodi, Julian Schur, Max Argus, Thomas Brox,
• Abstract summary: We 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.
• Score: 26.156636891713745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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