Label-free Concept Bottleneck Models



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Concept bottleneck models (CBM) are a popular way of creating more interpretable neural networks by having hidden layer neurons correspond to humanunderstandable concepts. However, existing CBMs and their variants have two crucial limitations: first, they need to collect labeled data for each of the predefined concepts, which is time consuming and labor intensive; second, the accuracy of a CBM is often significantly lower than that of a standard neural network, especially on more complex datasets. This poor performance creates a barrier for adopting CBMs in practical real world applications. Motivated by these challenges, we propose Label-free CBM which is a novel framework to transform any neural network into an interpretable CBM without labeled concept data, while retaining a high accuracy. Our Label-free CBM has many advantages, it is: scalable – we present the first CBM scaled to ImageNet, efficient – creating a CBM takes only a few hours even for very large datasets, and automated – training it for a new dataset requires minimal human effort. Our code is available at

Please cite our work using the BibTeX below.

title={Label-free Concept Bottleneck Models},
author={Tuomas Oikarinen and Subhro Das and Lam M. Nguyen and Tsui-Wei Weng},
booktitle={The Eleventh International Conference on Learning Representations },
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