论文标题
解释DNN中的多元沙普利相互作用
Interpreting Multivariate Shapley Interactions in DNNs
论文作者
论文摘要
本文旨在从多元相互作用的角度来解释深层神经网络（DNN）。在本文中，我们定义和量化了DNN多个输入变量之间相互作用的重要性。具有较强相互作用的输入变量通常会形成一个联盟并反映原型特征，原型特征是DNN记忆和使用的。我们根据Shapley值来定义相互作用的重要性，该值旨在为推理分配每个输入变量的归因值。我们已经对各种DNN进行了实验。实验结果证明了该方法的有效性。
This paper aims to explain deep neural networks (DNNs) from the perspective of multivariate interactions. In this paper, we define and quantify the significance of interactions among multiple input variables of the DNN. Input variables with strong interactions usually form a coalition and reflect prototype features, which are memorized and used by the DNN for inference. We define the significance of interactions based on the Shapley value, which is designed to assign the attribution value of each input variable to the inference. We have conducted experiments with various DNNs. Experimental results have demonstrated the effectiveness of the proposed method.
