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Measuring similarity of neural networks to understand and improve theirbehavior has become an issue of great importance and research interest. In thissurvey, we provide a comprehensive overview of two complementary perspectivesof measuring neural network similarity: (i) representational similarity, whichconsiders how activations of intermediate layers differ, and (ii) functionalsimilarity, which considers how models differ in their outputs. In addition toproviding detailed descriptions of existing measures, we summarize and discussresults on the properties of and relationships between these measures, andpoint to open research problems. We hope our work lays a foundation for moresystematic research on the properties and applicability of similarity measuresfor neural network models.

Similarity of Neural Network Models: A Survey of Functional and Representational Measures