  Recently, many methods to interpret and visualize deep neural network
predictions have been proposed and significant progress has been made. However,
a more class-discriminative and visually pleasing explanation is required.
Thus, this paper proposes a region-based approach that estimates feature
importance in terms of appropriately segmented regions. By fusing the saliency
maps generated from multi-scale segmentations, a more class-discriminative and
visually pleasing map is obtained. We incorporate this regional multi-scale
concept into a prediction difference method that is model-agnostic. An input
image is segmented in several scales using the super-pixel method, and
exclusion of a region is simulated by sampling a normal distribution
constructed using the boundary prior. The experimental results demonstrate that
the regional multi-scale method produces much more class-discriminative and
visually pleasing saliency maps.
