  Integrated Gradients (IG) is a commonly used feature attribution method for
deep neural networks. While IG has many desirable properties, the method often
produces spurious/noisy pixel attributions in regions that are not related to
the predicted class when applied to visual models. While this has been
previously noted, most existing solutions are aimed at addressing the symptoms
by explicitly reducing the noise in the resulting attributions. In this work,
we show that one of the causes of the problem is the accumulation of noise
along the IG path. To minimize the effect of this source of noise, we propose
adapting the attribution path itself -- conditioning the path not just on the
image but also on the model being explained. We introduce Adaptive Path Methods
(APMs) as a generalization of path methods, and Guided IG as a specific
instance of an APM. Empirically, Guided IG creates saliency maps better aligned
with the model's prediction and the input image that is being explained. We
show through qualitative and quantitative experiments that Guided IG
outperforms other, related methods in nearly every experiment.
