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arXiv · 2002.09168

Residual Knowledge Distillation

Mengya Gao·Yujun Shen·Quanquan Li·Chen Change Loy
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Abstract

Knowledge distillation (KD) is one of the most potent ways for model compression. The key idea is to transfer the knowledge from a deep teacher model (T) to a shallower student (S). However, existing methods suffer from performance degradation due to the substantial gap between the learning capacities of S and T. To remedy this problem, this work proposes Residual Knowledge Distillation (RKD), which further distills the knowledge by introducing an assistant (A). Specifically, S is trained to mimic the feature maps of T, and A aids this process by learning the residual error between them. In this way, S and A complement with each other to get better knowledge from T. Furthermore, we devise an effective method to derive S and A from a given model without increasing the total computational cost. Extensive experiments show that our approach achieves appealing results on popular classification datasets, CIFAR-100 and ImageNet, surpassing state-of-the-art methods.

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Mengya Gao, Yujun Shen, Quanquan Li, Chen Change Loy. 2020-02-21. Residual Knowledge Distillation. https://arxiv.org/abs/2002.09168

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