Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation

TL;DR

This paper introduces a self distillation framework that improves CNN accuracy by internal knowledge transfer, enabling smaller networks to perform better without increasing their size or complexity.

Contribution

The proposed self distillation method distills knowledge within a network, enhancing accuracy while reducing model size, unlike traditional external teacher-student distillation approaches.

Findings

Average accuracy improvement of 2.65% across tested models

Maximum improvement of 4.07% in VGG19

Enables scalable inference on resource-limited devices

Abstract

Convolutional neural networks have been widely deployed in various application scenarios. In order to extend the applications' boundaries to some accuracy-crucial domains, researchers have been investigating approaches to boost accuracy through either deeper or wider network structures, which brings with them the exponential increment of the computational and storage cost, delaying the responding time. In this paper, we propose a general training framework named self distillation, which notably enhances the performance (accuracy) of convolutional neural networks through shrinking the size of the network rather than aggrandizing it. Different from traditional knowledge distillation - a knowledge transformation methodology among networks, which forces student neural networks to approximate the softmax layer outputs of pre-trained teacher neural networks, the proposed self distillation framework distills knowledge within network itself. The networks are firstly divided into several sections. Then the knowledge in the deeper portion of the networks is squeezed into the shallow ones. Experiments further prove the generalization of the proposed self distillation framework: enhancement of accuracy at average level is 2.65%, varying from 0.61% in ResNeXt as minimum to 4.07% in VGG19 as maximum. In addition, it can also provide flexibility of depth-wise scalable inference on resource-limited edge devices.Our codes will be released on github soon.