Assessment of the Reliablity of a Model's Decision by Generalizing Attribution to the Wavelet Domain

• URL: http://arxiv.org/abs/2305.14979v5
• Date: Thu, 9 Nov 2023 13:07:22 GMT
• Title: Assessment of the Reliablity of a Model's Decision by Generalizing Attribution to the Wavelet Domain
• Authors: Gabriel Kasmi and Laurent Dubus and Yves-Marie Saint Drenan and Philippe Blanc
• Abstract summary: We introduce the Wavelet sCale Attribution Method (WCAM), a generalization of attribution from the pixel domain to the space-scale domain using wavelet transforms. Our code is accessible here.
• Score: 0.8192907805418583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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}.

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