On the Inadequacy of CKA as a Measure of Similarity in Deep Learning

Abstract

Comparing learned representations is a challenging problem which has been approached in different ways. The CKA similarity metric, particularly it's linear variant, has recently become a popular approach and has been widely used to compare representations of a network's different layers, of similar networks trained differently, or of models with different architectures trained on the same data. CKA results have been used to make a wide variety of claims about similarity and dissimilarity of these various representations. In this work we investigate several weaknesses of the CKA similarity metric, demonstrating situations in which it gives unexpected or counterintuitive results. We then study approaches for modifying representations to maintain functional behaviour while changing the CKA value. Indeed we illustrate in some cases the CKA value can be heavily manipulated without substantial changes to the functional behaviour.