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.

翻译：本文旨在从多变量相互作用的角度解释深神经网络(DNNs),在本文中,我们界定和量化DNN多个输入变量之间相互作用的重要性。具有强大相互作用的投入变量通常形成一个联盟,反映原型特征,由DNN用这些原型进行记忆和推断。我们根据“光谱值”界定相互作用的意义,该数值旨在为推断确定每个输入变量的属性值。我们与各DNNs进行了实验。实验结果显示了拟议方法的有效性。