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.

翻译：识别深度神经网络（DNNs）中网络单元的角色，在理解DNNs的机制以及建立深度学习与神经科学的基本联系等多个方面都至关重要。然而，具有不同泛化能力的DNNs中单元可能呈现哪些角色仍不清楚。为此，我们通过引入功能检索测试来对DNNs中的单元进行角色分类，根据单元在训练集和测试集上分别表现出的功能偏好，将单元分为四类。我们证明了这四类单元的比率从两个不同角度与DNNs的泛化能力高度相关，并据此给出了良好泛化能力DNNs的特征指标。