Post-hoc Self-explanation of CNNs

Ahcène Boubekki; Line H. Clemmensen

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arXiv · 2603.28466

Post-hoc Self-explanation of CNNs

Ahcène Boubekki·Line H. Clemmensen

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Abstract

Although standard Convolutional Neural Networks (CNNs) can be mathematically reinterpreted as Self-Explainable Models (SEMs), their built-in prototypes do not on their own accurately represent the data. Replacing the final linear layer with a $k$-means-based classifier addresses this limitation without compromising performance. This work introduces a common formalization of $k$-means-based post-hoc explanations for the classifier, the encoder's final output (B4), and combinations of intermediate feature activations. The latter approach leverages the spatial consistency of convolutional receptive fields to generate concept-based explanation maps, which are supported by gradient-free feature attribution maps. Empirical evaluation with a ResNet34 shows that using shallower, less compressed feature activations, such as those from the last three blocks (B234), results in a trade-off between semantic fidelity and a slight reduction in predictive performance.

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BibTeX RIS

Ahcène Boubekki, Line H. Clemmensen. 2026-03-30. Post-hoc Self-explanation of CNNs. https://arxiv.org/abs/2603.28466

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