Concept Bottleneck Models Without Predefined Concepts

TL;DR

This paper introduces an unsupervised approach to concept bottleneck models that automatically discovers concepts without human annotations, improving interpretability and performance while reducing the number of concepts needed.

Contribution

It proposes a novel unsupervised concept discovery method and input-dependent concept selection, eliminating the need for predefined concepts in CBMs.

Findings

Improves downstream classification performance.

Reduces the number of concepts needed for accurate classification.

Narrows the gap between interpretable models and black-box models.

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.