  Revealing the transparency of Deep Neural Networks (DNNs) has been widely
studied to describe the decision mechanisms of network inner structures. In
this paper, we propose a novel post-hoc framework, Unfold and Conquer
Attribution Guidance (UCAG), which enhances the explainability of the network
decision by spatially scrutinizing the input features with respect to the model
confidence. Addressing the phenomenon of missing detailed descriptions, UCAG
sequentially complies with the confidence of slices of the image, leading to
providing an abundant and clear interpretation. Therefore, it is possible to
enhance the representation ability of explanation by preserving the detailed
descriptions of assistant input features, which are commonly overwhelmed by the
main meaningful regions. We conduct numerous evaluations to validate the
performance in several metrics: i) deletion and insertion, ii) (energy-based)
pointing games, and iii) positive and negative density maps. Experimental
results, including qualitative comparisons, demonstrate that our method
outperforms the existing methods with the nature of clear and detailed
explanations and applicability.
