Faithfully summarizing the knowledge encoded by a deep neural network (DNN) into a few symbolic primitive patterns without losing much information represents a core challenge in explainable AI. To this end, Ren et al. (2024) have derived a series of theorems to prove that the inference score of a DNN can be explained as a small set of interactions between input variables. However, the lack of generalization power makes it still hard to consider such interactions as faithful primitive patterns encoded by the DNN. Therefore, given different DNNs trained for the same task, we develop a new method to extract interactions that are shared by these DNNs. Experiments show that the extracted interactions can better reflect common knowledge shared by different DNNs.

翻译：忠实且不失真地将深度神经网络（DNN）编码的知识总结为少量符号化的基元模式，是可解释人工智能领域的核心挑战之一。为此，Ren等人（2024）推导了一系列定理，证明DNN的推理得分可以解释为输入变量之间的一小组交互作用。然而，由于泛化能力不足，此类交互仍难以被视为DNN所编码的忠实基元模式。因此，针对为同一任务训练的不同DNN，我们开发了一种新方法来提取这些DNN所共享的交互作用。实验表明，提取出的交互能更好地反映不同DNN所共享的共性知识。