Feb. 2, 2024, 9:42 p.m. | Roy Miles Krystian Mikolajczyk

cs.CV updates on arXiv.org arxiv.org

In this paper we revisit the efficacy of knowledge distillation as a function matching and metric learning problem. In doing so we verify three important design decisions, namely the normalisation, soft maximum function, and projection layers as key ingredients. We theoretically show that the projector implicitly encodes information on past examples, enabling relational gradients for the student. We then show that the normalisation of representations is tightly coupled with the training dynamics of this projector, which can have a large …

cs.ai cs.cv decisions design distillation examples function information key knowledge paper projection role show understanding verify

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