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A SYSTEM AND METHOD FOR EVALUATING A PERFORMANCE OF EXPLAINABILITY METHODS USED WITH ARTIFICIAL NEURAL NETWORKS
A SYSTEM AND METHOD FOR EVALUATING A PERFORMANCE OF EXPLAINABILITY METHODS USED WITH ARTIFICIAL NEURAL NETWORKS
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机译:用于评估与人工神经网络一起使用的解释性方法的性能的系统和方法
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摘要
A computing system configured to perform the steps of dividing both a saliency map and a ground-truth feature map into cells in order to obtain segmented saliency map and a segmented feature map, wherein a relevance score is assigned to each cell based on values of individual pixels within the cells in the saliency map and feature map, selecting, for both the segmented saliency map and segmented feature map, a selected number of selected cells corresponding to the most relevant cells having highest relevance scores within the segmented saliency map and the segmented feature map, respectively, and computing a level of agreement between the segmented saliency map and the segmented feature map by comparing the selected cells having highest relevance scores in the segmented saliency map to the selected cells having highest relevance scores in the segmented feature map.
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