  Integrated Gradients as an attribution method for deep neural network models
offers simple implementability. However, it suffers from noisiness of
explanations which affects the ease of interpretability. The SmoothGrad
technique is proposed to solve the noisiness issue and smoothen the attribution
maps of any gradient-based attribution method. In this paper, we present
SmoothTaylor as a novel theoretical concept bridging Integrated Gradients and
SmoothGrad, from the Taylor's theorem perspective. We apply the methods to the
image classification problem, using the ILSVRC2012 ImageNet object recognition
dataset, and a couple of pretrained image models to generate attribution maps.
These attribution maps are empirically evaluated using quantitative measures
for sensitivity and noise level. We further propose adaptive noising to
optimize for the noise scale hyperparameter value. From our experiments, we
find that the SmoothTaylor approach together with adaptive noising is able to
generate better quality saliency maps with lesser noise and higher sensitivity
to the relevant points in the input space as compared to Integrated Gradients.
