  Identifying the role of network units in deep neural networks (DNNs) is
critical in many aspects including giving understandings on the mechanisms of
DNNs and building basic connections between deep learning and neuroscience.
However, there remains unclear on which roles the units in DNNs with different
generalization ability could present. To this end, we give role taxonomy of
units in DNNs via introducing the retrieval-of-function test, where units are
categorized into four types in terms of their functional preference on
separately the training set and testing set. We show that ratios of the four
categories are highly associated with the generalization ability of DNNs from
two distinct perspectives, based on which we give signs of DNNs with well
generalization.
