Attribution algorithms are frequently employed to explain the decisions of neural network models. Integrated Gradients (IG) is an influential attribution method due to its strong axiomatic foundation. The algorithm is based on integrating the gradients along a path from a reference image to the input image. Unfortunately, it can be observed that gradients computed from regions where the output logit changes minimally along the path provide poor explanations for the model decision, which is called the saturation effect problem. In this paper, we propose an attribution algorithm called integrated decision gradients (IDG). The algorithm focuses on integrating gradients from the region of the path where the model makes its decision, i.e., the portion of the path where the output logit rapidly transitions from zero to its final value. This is practically realized by scaling each gradient by the derivative of the output logit with respect to the path. The algorithm thereby provides a principled solution to the saturation problem. Additionally, we minimize the errors within the Riemann sum approximation of the path integral by utilizing non-uniform subdivisions determined by adaptive sampling. In the evaluation on ImageNet, it is demonstrated that IDG outperforms IG, left-IG, guided IG, and adversarial gradient integration both qualitatively and quantitatively using standard insertion and deletion metrics across three common models.

翻译：归因算法常用于解释神经网络模型的决策。积分梯度（IG）是一种具有影响力的归因方法，因其公理基础坚实而著称。该算法基于沿参考图像到输入图像路径的梯度积分。然而，可以观察到，沿路径输出logit变化极小区域的梯度对模型决策的解释性较差，这被称为饱和效应问题。本文提出一种名为集成决策梯度（IDG）的归因算法。该算法专注于集成路径中模型做出决策区域的梯度，即输出logit从零快速过渡到最终值的路径部分。实际实现中，通过将每个梯度乘以输出logit相对于路径的导数来达成。该算法由此为饱和问题提供了原则性解决方案。此外，我们利用自适应采样确定的非均匀划分，最小化路径积分离散化黎曼和近似中的误差。在ImageNet上的评估表明，IDG在定性及定量上均优于IG、left-IG、guided IG和对抗梯度积分，在三种常见模型上使用标准插入和删除指标进行了验证。