Label-free Concept Bottleneck Models

Tuomas Oikarinen, Subhro Das, Lam M. Nguyen, Tsui-Wei Weng

Keywords: Interpretability, Explainability, Concept Bottleneck Models

Abstract: Concept bottleneck models (CBM) are a popular way of creating more interpretable neural networks by having hidden layer neurons correspond to human-understandable 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 https://github.com/Trustworthy-ML-Lab/Label-free-CBM.

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TL;DR: Scalable, automated and efficient way to create Concept Bottleneck Models without labeled concept data.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2304.06129/code)