  As Deep Neural Networks (DNNs) have demonstrated superhuman performance in a
variety of fields, there is an increasing interest in understanding the complex
internal mechanisms of DNNs. In this paper, we propose Relative Attributing
Propagation (RAP), which decomposes the output predictions of DNNs with a new
perspective of separating the relevant (positive) and irrelevant (negative)
attributions according to the relative influence between the layers. The
relevance of each neuron is identified with respect to its degree of
contribution, separated into positive and negative, while preserving the
conservation rule. Considering the relevance assigned to neurons in terms of
relative priority, RAP allows each neuron to be assigned with a bi-polar
importance score concerning the output: from highly relevant to highly
irrelevant. Therefore, our method makes it possible to interpret DNNs with much
clearer and attentive visualizations of the separated attributions than the
conventional explaining methods. To verify that the attributions propagated by
RAP correctly account for each meaning, we utilize the evaluation metrics: (i)
Outside-inside relevance ratio, (ii) Segmentation mIOU and (iii) Region
perturbation. In all experiments and metrics, we present a sizable gap in
comparison to the existing literature. Our source code is available in
\url{https://github.com/wjNam/Relative_Attributing_Propagation}.
