A major limitation of deep neural networks in safety-critical contexts is their lack of transparency. Feature attribution methods address this by identifying which features influence a model’s decisions. For high-dimensional inputs like images, attributions often appear as pixel-based heatmaps. However, extending pixel-based methods to non-image domains, leads to suboptimal explanations. This paper introduces the Wavelet Attribution Method (WAM), which replaces pixels with wavelet coefficients as features for attribution. Wavelet coefficients, defined for square-integrable signals across dimensions, provide a mathematically grounded and semantically richer domain. As the wavelet domain is defined for any input dimension, it provides a unification of the feature space for attribution. We demonstrate WAM's effectiveness on audio, images, and volumes, where it quantitatively matches or outperforms existing gradient-based methods. Explanations generated by WAM leverage the spatial and scale-localized properties of wavelet coefficients, capturing both the where and what of a model's focus. We illustrate this by showing how WAM relates to model robustness, enables deeper volume interpretation, and identifies key audio components in noisy samples.
@inproceedings{
kasmi2025WAM,
title={One Wave To Explain Them All: A Unifying Perspective On Feature Attribution},
author={Kasmi, Gabriel and Brunetto, Amandine and Fel, Thomas and Parekh, Jayneel},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
}