Defining and Extracting generalizable interaction primitives from DNNs
Lu Chen Siyu Lou Benhao Huang Quanshi Zhang
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. (2023c)
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.