神经网络模型的相似性：功能性与表征性度量方法综述
Similarity of Neural Network Models: A Survey of Functional and Representational Measures
AI总结：
该综述梳理了神经网络相似性度量的表征与功能两大互补视角，介绍现有方法、总结其性质与关联并指出开放问题，为相关系统研究奠定基础。
摘要：
度量神经网络的相似性以理解并优化其行为，已成为备受关注的重要研究课题。本综述全面梳理了神经网络相似性度量的两种互补视角：（一）表征相似性，关注中间层激活的差异；（二）功能相似性，关注模型输出的差异。除了详细介绍现有度量方法外，我们还总结并讨论了这些度量的性质及相互关系的相关研究成果，并指出了待解决的研究问题。我们希望本工作能为更系统地研究神经网络模型相似性度量的性质与适用性奠定基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
