FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks

• URL: http://arxiv.org/abs/2505.17883v1
• Date: Fri, 23 May 2025 13:31:54 GMT
• Title: FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks
• Authors: Laines Schmalwasser, Niklas Penzel, Joachim Denzler, Julia Niebling,
• Abstract summary: Concept Activation Vectors (CAVs) can identify whether a model learned a concept or not.<n>FastCAV is a novel approach that accelerates the extraction of CAVs by up to 63.6x (on average 46.4x)
• Score: 10.20676488210292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Concepts such as objects, patterns, and shapes are how humans understand the world. Building on this intuition, concept-based explainability methods aim to study representations learned by deep neural networks in relation to human-understandable concepts. Here, Concept Activation Vectors (CAVs) are an important tool and can identify whether a model learned a concept or not. However, the computational cost and time requirements of existing CAV computation pose a significant challenge, particularly in large-scale, high-dimensional architectures. To address this limitation, we introduce FastCAV, a novel approach that accelerates the extraction of CAVs by up to 63.6x (on average 46.4x). We provide a theoretical foundation for our approach and give concrete assumptions under which it is equivalent to established SVM-based methods. Our empirical results demonstrate that CAVs calculated with FastCAV maintain similar performance while being more efficient and stable. In downstream applications, i.e., concept-based explanation methods, we show that FastCAV can act as a replacement leading to equivalent insights. Hence, our approach enables previously infeasible investigations of deep models, which we demonstrate by tracking the evolution of concepts during model training.

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