  The clear transparency of Deep Neural Networks (DNNs) is hampered by complex
internal structures and nonlinear transformations along deep hierarchies. In
this paper, we propose a new attribution method, Relative Sectional Propagation
(RSP), for fully decomposing the output predictions with the characteristics of
class-discriminative attributions and clear objectness. We carefully revisit
some shortcomings of backpropagation-based attribution methods, which are
trade-off relations in decomposing DNNs. We define hostile factor as an element
that interferes with finding the attributions of the target and propagate it in
a distinguishable way to overcome the non-suppressed nature of activated
neurons. As a result, it is possible to assign the bi-polar relevance scores of
the target (positive) and hostile (negative) attributions while maintaining
each attribution aligned with the importance. We also present the purging
techniques to prevent the decrement of the gap between the relevance scores of
the target and hostile attributions during backward propagation by eliminating
the conflicting units to channel attribution map. Therefore, our method makes
it possible to decompose the predictions of DNNs with clearer
class-discriminativeness and detailed elucidations of activation neurons
compared to the conventional attribution methods. In a verified experimental
environment, we report the results of the assessments: (i) Pointing Game, (ii)
mIoU, and (iii) Model Sensitivity with PASCAL VOC 2007, MS COCO 2014, and
ImageNet datasets. The results demonstrate that our method outperforms existing
backward decomposition methods, including distinctive and intuitive
visualizations.
