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中展现了卓越的性能，但由于其黑箱特性，在众多科学和技术领域的部署面临挑战。科学家和从业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损伤具有鲁棒性。现有归因方法旨在通过突出图像域中的重要区域提供人类可理解的解释，但未能全面刻画决策过程的可靠性。为填补这一空白，我们提出小波尺度归因方法（WCAM），该方法利用小波变换将归因从像素域泛化到空间-尺度域。小波域中的归因揭示了模型关注的区域{\it 以及}尺度，从而能够评估决策是否可靠。