Uncertainty Quantification for Gradient-based Explanations in Neural
  Networks
Mihir Mulye Matias Valdenegro-Toro
Explanation methods help understand the reasons for a model's prediction.
These methods are increasingly involved in model debugging, performance
optimization, and gaining insights into the workings of a model. With such
critical applications of these methods, it is imperative to measure the
uncertainty associated with the explanations generated by these methods. In
this paper, we propose a pipeline to ascertain the explanation uncertainty of
neural networks by combining uncertainty estimation methods and explanation
methods. We use this pipeline to produce explanation distributions for the
CIFAR-10, FER+, and California Housing datasets. By computing the coefficient
of variation of these distributions, we evaluate the confidence in the
explanation and determine that the explanations generated using Guided
Backpropagation have low uncertainty associated with them. Additionally, we
compute modified pixel insertion/deletion metrics to evaluate the quality of
the generated explanations.