  Saliency methods can aid understanding of deep neural networks. Recent years
have witnessed many improvements to saliency methods, as well as new ways for
evaluating them. In this paper, we 1) present a novel region-based attribution
method, XRAI, that builds upon integrated gradients (Sundararajan et al. 2017),
2) introduce evaluation methods for empirically assessing the quality of
image-based saliency maps (Performance Information Curves (PICs)), and 3)
contribute an axiom-based sanity check for attribution methods. Through
empirical experiments and example results, we show that XRAI produces better
results than other saliency methods for common models and the ImageNet dataset.
