Spot-adaptive Knowledge Distillation

TL;DR

This paper introduces spot-adaptive knowledge distillation (SAKD), a method that dynamically selects the layers for distillation based on each sample and training epoch, enhancing the performance of existing distillation techniques.

Contribution

The paper proposes a novel adaptive distillation strategy that determines distillation spots per sample and epoch, improving upon fixed-spot methods and integrating seamlessly with existing distillers.

Findings

SAKD improves performance across 10 state-of-the-art distillers.

It enhances distillation in both homogeneous and heterogeneous settings.

Experimental results validate the effectiveness of adaptive spot selection.

Abstract

Knowledge distillation (KD) has become a well established paradigm for compressing deep neural networks. The typical way of conducting knowledge distillation is to train the student network under the supervision of the teacher network to harness the knowledge at one or multiple spots (i.e., layers) in the teacher network. The distillation spots, once specified, will not change for all the training samples, throughout the whole distillation process. In this work, we argue that distillation spots should be adaptive to training samples and distillation epochs. We thus propose a new distillation strategy, termed spot-adaptive KD (SAKD), to adaptively determine the distillation spots in the teacher network per sample, at every training iteration during the whole distillation period. As SAKD actually focuses on "where to distill" instead of "what to distill" that is widely investigated by most existing works, it can be seamlessly integrated into existing distillation methods to further improve their performance. Extensive experiments with 10 state-of-the-art distillers are conducted to demonstrate the effectiveness of SAKD for improving their distillation performance, under both homogeneous and heterogeneous distillation settings. Code is available at https://github.com/zju-vipa/spot-adaptive-pytorch