  This paper quantifies the quality of heatmap-based eXplainable AI (XAI)
methods w.r.t image classification problem. Here, a heatmap is considered
desirable if it improves the probability of predicting the correct classes.
Different XAI heatmap-based methods are empirically shown to improve
classification confidence to different extents depending on the datasets, e.g.
Saliency works best on ImageNet and Deconvolution on Chest X-Ray Pneumonia
dataset. The novelty includes a new gap distribution that shows a stark
difference between correct and wrong predictions. Finally, the generative
augmentative explanation is introduced, a method to generate heatmaps capable
of improving predictive confidence to a high level.
