  Hedging is a strategy for reducing the potential risks in various types of
investments by adopting an opposite position in a related asset. Motivated by
the equity technique, we introduce a method for decomposing output predictions
into intensive salient attributions by hedging the evidence for a decision. We
analyze the conventional approach applied to the evidence for a decision and
discuss the paradox of the conservation rule. Subsequently, we define the
viewpoint of evidence as a gap of positive and negative influence among the
gradient-derived initial contribution maps and propagate the antagonistic
elements to the evidence as suppressors, following the criterion of the degree
of positive attribution defined by user preference. In addition, we reflect the
severance or sparseness contribution of inactivated neurons, which are mostly
irrelevant to a decision, resulting in increased robustness to
interpretability. We conduct the following assessments in a verified
experimental environment: pointing game, most relevant first region insertion,
outside-inside relevance ratio, and mean average precision on the PASCAL VOC
2007, MS COCO 2014, and ImageNet datasets. The results demonstrate that our
method outperforms existing attribution methods in distinctive, intensive, and
intuitive visualization with robustness and applicability in general models.
