  Attribution algorithms are frequently employed to explain the decisions of
neural network models. Integrated Gradients (IG) is an influential attribution
method due to its strong axiomatic foundation. The algorithm is based on
integrating the gradients along a path from a reference image to the input
image. Unfortunately, it can be observed that gradients computed from regions
where the output logit changes minimally along the path provide poor
explanations for the model decision, which is called the saturation effect
problem. In this paper, we propose an attribution algorithm called integrated
decision gradients (IDG). The algorithm focuses on integrating gradients from
the region of the path where the model makes its decision, i.e., the portion of
the path where the output logit rapidly transitions from zero to its final
value. This is practically realized by scaling each gradient by the derivative
of the output logit with respect to the path. The algorithm thereby provides a
principled solution to the saturation problem. Additionally, we minimize the
errors within the Riemann sum approximation of the path integral by utilizing
non-uniform subdivisions determined by adaptive sampling. In the evaluation on
ImageNet, it is demonstrated that IDG outperforms IG, Left-IG, Guided IG, and
adversarial gradient integration both qualitatively and quantitatively using
standard insertion and deletion metrics across three common models.
