@inproceedings{schmalwasser2025fastcav,
    type = {inproceedings},
    key = {schmalwasser2025fastcav},
    author = {Laines Schmalwasser and Niklas Penzel and Joachim Denzler and Julia Niebling},
    title = {FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks},
    booktitle = {Proceedings of the 42nd International Conference on Machine Learning (ICML)},
    year = {2025},
    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 \methodname, a novel approach that accelerates the extraction of CAVs by up to \maxspeedup (on average \avgspeedup). %times. 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 \methodname maintain similar performance while being more efficient and stable. In downstream applications, i.e., concept-based explanation methods, we show that \methodname 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.},
    doi = {},
    url = {},
    arxiv = {},
    isbn = {},
    issn = {},
    langid = {english},
    publish = {},
    code = {},
    note = {(Accepted at ICML 2025)},
}