  Integrated Gradients has become a popular method for post-hoc model
interpretability. De-spite its popularity, the composition and relative impact
of different regions of the integral path are not well understood. We explore
these effects and find that gradients in saturated regions of this path, where
model output changes minimally, contribute disproportionately to the computed
attribution. We propose a variant of IntegratedGradients which primarily
captures gradients in unsaturated regions and evaluate this method on ImageNet
classification networks. We find that this attribution technique shows higher
model faithfulness and lower sensitivity to noise com-pared with standard
Integrated Gradients. A note-book illustrating our computations and results is
available at https://github.com/vivekmig/captum-1/tree/ExpandedIG.
