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Knowledge Distillation: Bad Models Can Be Good Role Models by Kaplun, Gal; Malach, Eran; Nakkiran, Preetum; Shalev-Shwartz, Shai is a scholarly article available to read on EtoBox.

What is Knowledge Distillation: Bad Models Can Be Good Role Models about?

Large neural networks trained in the overparameterized regime are able to fit noise to zero train error. Recent work \citep{nakkiran2020distributional} has empirically observed that such networks behave as "conditional samplers" from the noisy distribution. That is, they replicate the noise in the train data to unseen examples. We give a theoretical framework for studying this conditional sampling behavior in the context of learning theory. We relate the notion of such samplers to knowledge distillation, where a student network imitates the outputs of a teacher on unlabeled data. We show that samplers, while being bad classifiers, can be good teachers. Concretely, we prove that distillation from samplers is guaranteed to produce a student which approximates the Bayes optimal classifier. Finally, we show that some common learning algorithms (e.g., Nearest-Neighbours and Kernel Machines) can generate samplers when applied in the overparameterized regime.

Author

Kaplun, Gal; Malach, Eran; Nakkiran, Preetum; Shalev-Shwartz, Shai

Published

2022

Language

EN