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Analysis of how semantic concepts are represented within Convolutional Neural Networks (CNNs) is a widely used approach in Explainable Artificial Intelligence (XAI) for interpreting CNNs.
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Zhang, R., Madumal, P., Miller, T., Ehinger, K.A., Rubinstein, B.I.: Invertible concept-based explanations for cnn models with non-negative concept activation vectors. In: Proc. AAAI Conf. Artificial Intelligence. pp. 11682–11690 (2021)
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Abid, A., Yuksekgonul, M., Zou, J.: Meaningfully debugging model mistakes using conceptual counterfactual explanations. In: Proc. 39th Int. Conf. Machine Learning. pp. 66–88. PMLR (Jun 2022)
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Schwalbe, G.: Concept Embedding Analysis: A Review. arXiv:2203.13909 [cs, stat] (Mar 2022)
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Schwalbe, G., Finzel, B.: A comprehensive taxonomy for explainable artificial intelligence: A systematic survey of surveys on methods and concepts. Data Mining and Knowledge Discovery (Jan 2023). https://doi.org/10.1007/s10618-022-00867-8
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