Interpretability methods for deep neural networks mainly focus on the
sensitivity of the class score with respect to the original or perturbed input,
usually measured using actual or modified gradients. Some methods also use a
model-agnostic approach to understanding the rationale behind every prediction.
In this paper, we argue and demonstrate that local geometry of the model
parameter space relative to the input can also be beneficial for improved
post-hoc explanations. To achieve this goal, we introduce an interpretability
method called "geometrically-guided integrated gradients" that builds on top of
the gradient calculation along a linear path as traditionally used in
integrated gradient methods. However, instead of integrating gradient
information, our method explores the model's dynamic behavior from multiple
scaled versions of the input and captures the best possible attribution for
each input. We demonstrate through extensive experiments that the proposed
approach outperforms vanilla and integrated gradients in subjective and
quantitative assessment. We also propose a "model perturbation" sanity check to
complement the traditionally used "model randomization" test.

本文提出了一种基于深度神经网络模型参数空间局部几何性质的解释性方法 —— 几何引导的积分梯度法，在主观和量化评价方面表现优于传统的积分梯度法和总体梯度法，并提出了 “模型扰动” 的健全性检查，以补充传统的 “模型随机化” 测试。