Measuring similarity of neural networks to understand and improve their behavior has become an issue of great importance and research interest. In this survey, we provide a comprehensive overview of two complementary perspectives of measuring neural network similarity: (i) representational similarity, which considers how activations of intermediate layers differ, and (ii) functional similarity, which considers how models differ in their outputs. In addition to providing detailed descriptions of existing measures, we summarize and discuss results on the properties of and relationships between these measures, and point to open research problems. We hope our work lays a foundation for more systematic research on the properties and applicability of similarity measures for neural network models.

翻译：度量神经网络相似性以理解和改进其行为已成为至关重要且备受关注的研究课题。在本综述中，我们全面梳理了度量神经网络相似性的两个互补视角：(i) 表征相似性，关注中间层激活的差异；(ii) 功能相似性，关注模型输出行为的差异。除详细阐述现有度量方法外，我们总结并讨论了这些度量的特性与相互关系的研究成果，并指出有待探索的研究问题。我们希望本工作能为神经网络模型相似性度量的特性与适用性研究奠定系统性基础。