Numerous approaches have attempted to interpret deep neural networks (DNNs) by attributing the prediction of DNN to its input features. One of the well-studied attribution methods is Integrated Gradients (IG). Specifically, the choice of baselines for IG is a critical consideration for generating meaningful and unbiased explanations for model predictions in different scenarios. However, current practice of exploiting a single baseline fails to fulfill this ambition, thus demanding multiple baselines. Fortunately, the inherent connection between IG and Aumann-Shapley Value forms a unique perspective to rethink the design of baselines. Under certain hypothesis, we theoretically analyse that a set of baseline aligns with the coalitions in Shapley Value. Thus, we propose a novel baseline construction method called Shapley Integrated Gradients (SIG) that searches for a set of baselines by proportional sampling to partly simulate the computation path of Shapley Value. Simulations on GridWorld show that SIG approximates the proportion of Shapley Values. Furthermore, experiments conducted on various image tasks demonstrate that compared to IG using other baseline methods, SIG exhibits an improved estimation of feature's contribution, offers more consistent explanations across diverse applications, and is generic to distinct data types or instances with insignificant computational overhead.

翻译：众多方法尝试通过将深度神经网络（DNN）的预测归因于其输入特征来解释DNN。积分梯度（IG）是研究较为深入的归因方法之一。其中，IG的基线选择对于在不同场景下生成有意义的、无偏的模型解释至关重要。然而，当前采用单一基线的方法未能实现这一目标，因而需要多基线方案。幸运的是，IG与Aumann-Shapley值之间的内在联系为重新思考基线设计提供了独特视角。在特定假设下，我们理论上证明了基线集合与Shapley值中的联盟结构相对应。据此，我们提出一种名为Shapley积分梯度（SIG）的新型基线构建方法，通过比例采样搜索基线集合，以部分模拟Shapley值的计算路径。在GridWorld上的仿真表明，SIG能近似Shapley值的比例关系。此外，在多种图像任务上的实验表明，相较于采用其他基线方法的IG，SIG能更准确地估计特征贡献度，在各类应用中提供更一致的解释，且对不同数据类型或实例具有普适性，计算开销微乎其微。