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Integrated Gradients (IG) as well as its variants are well-known techniquesfor interpreting the decisions of deep neural networks. While IG-basedapproaches attain state-of-the-art performance, they often integrate noise intotheir explanation saliency maps, which reduce their interpretability. Tominimize the noise, we examine the source of the noise analytically and proposea new approach to reduce the explanation noise based on our analyticalfindings. We propose the Important Direction Gradient Integration (IDGI)framework, which can be easily incorporated into any IG-based method that usesthe Reimann Integration for integrated gradient computation. Extensiveexperiments with three IG-based methods show that IDGI improves themdrastically on numerous interpretability metrics.

IDGI: A Framework to Eliminate Explanation Noise from Integrated Gradients