Attribution methods are primarily designed to study the distribution of input component contributions to individual model predictions. However, some research applications require a summary of attribution patterns across the entire dataset to facilitate the interpretability of the scrutinized models. In this paper, we present a new method called Integrated Gradient Correlation (IGC) that relates dataset-wise attributions to a model prediction score and enables region-specific analysis by a direct summation over associated components. We demonstrate our method on scalar predictions with the study of image feature representation in the brain from fMRI neural signals and the estimation of neural population receptive fields (NSD dataset), as well as on categorical predictions with the investigation of handwritten digit recognition (MNIST dataset). The resulting IGC attributions show selective patterns, revealing underlying model strategies coherent with their respective objectives.

本文介绍了一种名为集成梯度相关性（IGC）的新方法，它将数据集属性与模型预测得分相关联，并通过与相关组件的直接求和实现区域特定分析。在脑部fMRI神经信号的图像特征表示以及神经人口感受野（NSD数据集）的估计以及手写数字识别（MNIST数据集）的研究中，我们演示了我们的方法。得出的IGC属性显示出选择性模式，揭示了与各自目标一致的潜在模型策略。

综合梯度相关性：一种数据集层面的归因方法