PrUE: Distilling Knowledge from Sparse Teacher Networks. (arXiv:2207.00586v1 [cs.CV])

Although deep neural networks have enjoyed remarkable success across a wide
variety of tasks, their ever-increasing size also imposes significant overhead
on deployment. To compress these models, knowledge distillation was proposed to
transfer knowledge from a cumbersome (teacher) network into a lightweight
(student) network. However, guidance from a teacher does not always improve the
generalization of students, especially when the size gap between student and
teacher is large. Previous works argued that it was due to the high certainty
of the teacher, resulting in harder labels that were difficult to fit. To
soften these labels, we present a pruning method termed Prediction Uncertainty
Enlargement (PrUE) to simplify the teacher. Specifically, our method aims to
decrease the teacher’s certainty about data, thereby generating soft
predictions for students. We empirically investigate the effectiveness of the
proposed method with experiments on CIFAR-10/100, Tiny-ImageNet, and ImageNet.
Results indicate that student networks trained with sparse teachers achieve
better performance. Besides, our method allows researchers to distill knowledge
from deeper networks to improve students further. Our code is made public at:



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