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 and on what scales the model focuses, thus enabling us to assess whether a decision is reliable. Our code is accessible here: \url{https://github.com/gabrielkasmi/spectral-attribution}.

翻译：神经网络在计算机视觉领域展现了卓越性能，但其黑箱特性使其在众多科学与技术领域的部署面临挑战。科研人员与从业人员需要评估决策的可靠性，即同时判定模型是否依赖相关特征，以及这些特征对图像退化的鲁棒性。现有归因方法通过突出图像域中重要区域提供人类可理解的解释，但未能完全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至空间-尺度域。小波域中的归因可揭示模型关注的区域与尺度层级，从而评估决策的可靠性。我们的代码可通过以下链接获取：\url{https://github.com/gabrielkasmi/spectral-attribution}。