Despite the growing use of deep neural networks in safety-critical decision-making, their inherent black-box nature hinders transparency and interpretability. Explainable AI (XAI) methods have thus emerged to understand a model's internal workings, and notably attribution methods also called saliency maps. Conventional attribution methods typically identify the locations -- the where -- of significant regions within an input. However, because they overlook the inherent structure of the input data, these methods often fail to interpret what these regions represent in terms of structural components (e.g., textures in images or transients in sounds). Furthermore, existing methods are usually tailored to a single data modality, limiting their generalizability. In this paper, we propose leveraging the wavelet domain as a robust mathematical foundation for attribution. Our approach, the Wavelet Attribution Method (WAM) extends the existing gradient-based feature attributions into the wavelet domain, providing a unified framework for explaining classifiers across images, audio, and 3D shapes. Empirical evaluations demonstrate that WAM matches or surpasses state-of-the-art methods across faithfulness metrics and models in image, audio, and 3D explainability. Finally, we show how our method explains not only the where -- the important parts of the input -- but also the what -- the relevant patterns in terms of structural components.

翻译：尽管深度神经网络在安全关键决策中的应用日益广泛，但其固有的黑箱特性阻碍了透明度和可解释性。因此，可解释人工智能（XAI）方法应运而生，旨在理解模型的内部运作机制，其中显著图（亦称归因方法）尤为突出。传统的归因方法通常识别输入中重要区域的位置——即“何处”。然而，由于这些方法忽视了输入数据的内在结构，它们往往无法解释这些区域在结构组件（如图像中的纹理或声音中的瞬态）方面代表什么。此外，现有方法通常仅针对单一数据模态设计，限制了其泛化能力。本文提出利用小波域作为归因的鲁棒数学基础。我们的方法——小波归因方法（WAM）——将现有的基于梯度的特征归因扩展到小波域，为解释图像、音频和三维形状的分类器提供了一个统一框架。实证评估表明，在图像、音频和三维可解释性方面，WAM在忠实度指标和模型上均达到或超越了现有最先进方法。最后，我们展示了该方法不仅解释了“何处”——输入的重要部分——还解释了“什么”——结构组件方面的相关模式。