Neural networks have shown remarkable performance in computer vision, but their deployment in numerous scientific and technical fields is challenging due to their black-box nature. Scientists and practitioners need to evaluate the reliability of a decision, i.e., to know simultaneously if a model relies on the relevant features and whether these features are robust to image corruptions. Existing attribution methods aim to provide human-understandable explanations by highlighting important regions in the image domain, but fail to fully characterize a decision process's reliability. To bridge this gap, we introduce the Wavelet sCale Attribution Method (WCAM), a generalization of attribution from the pixel domain to the space-scale domain using wavelet transforms. Attribution in the wavelet domain reveals where {\it and} on what scales the model focuses, thus enabling us to assess whether a decision is reliable.

翻译：神经网络在计算机视觉领域展现出卓越性能，但由于其黑箱特性，在诸多科学与工程技术领域的部署面临挑战。科研人员与实践者需要评估决策的可靠性——即同时判断模型是否依赖相关特征，以及这些特征是否对图像退化具有鲁棒性。现有归因方法通过突出图像域中的重要区域提供可解释性，但未能完整刻画决策过程的可靠性。为弥合这一差距，我们提出小波尺度归因方法（WCAM），利用小波变换将归因从像素域推广至空间-尺度域。小波域中的归因揭示了模型关注的区域{\it 及}尺度层级，从而能够评估决策的可靠性。