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As Deep Neural Networks (DNNs) have demonstrated superhuman performance in avariety of fields, there is an increasing interest in understanding the complexinternal mechanisms of DNNs. In this paper, we propose Relative AttributingPropagation (RAP), which decomposes the output predictions of DNNs with a newperspective of separating the relevant (positive) and irrelevant (negative)attributions according to the relative influence between the layers. Therelevance of each neuron is identified with respect to its degree ofcontribution, separated into positive and negative, while preserving theconservation rule. Considering the relevance assigned to neurons in terms ofrelative priority, RAP allows each neuron to be assigned with a bi-polarimportance score concerning the output: from highly relevant to highlyirrelevant. Therefore, our method makes it possible to interpret DNNs with muchclearer and attentive visualizations of the separated attributions than theconventional explaining methods. To verify that the attributions propagated byRAP correctly account for each meaning, we utilize the evaluation metrics: (i)Outside-inside relevance ratio, (ii) Segmentation mIOU and (iii) Regionperturbation. In all experiments and metrics, we present a sizable gap incomparison to the existing literature. Our source code is available in\url{https://github.com/wjNam/Relative_Attributing_Propagation}.

Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks