Multi scale Feature Extraction and Fusion for Online Knowledge Distillation

TL;DR

This paper introduces MFEF, a novel online knowledge distillation method that enhances feature representation through multi-scale extraction, dual-attention, and fusion, leading to improved student model training.

Contribution

It proposes a new multi-scale feature extraction and fusion approach with dual-attention for online knowledge distillation, addressing limitations of existing methods.

Findings

MFEF outperforms existing methods on CIFAR datasets.

Enhanced feature representations improve student model performance.

The approach is effective across various network architectures.

Abstract

Online knowledge distillation conducts knowledge transfer among all student models to alleviate the reliance on pre-trained models. However, existing online methods rely heavily on the prediction distributions and neglect the further exploration of the representational knowledge. In this paper, we propose a novel Multi-scale Feature Extraction and Fusion method (MFEF) for online knowledge distillation, which comprises three key components: Multi-scale Feature Extraction, Dual-attention and Feature Fusion, towards generating more informative feature maps for distillation. The multiscale feature extraction exploiting divide-and-concatenate in channel dimension is proposed to improve the multi-scale representation ability of feature maps. To obtain more accurate information, we design a dual-attention to strengthen the important channel and spatial regions adaptively. Moreover, we aggregate and fuse the former processed feature maps via feature fusion to assist the training of student models. Extensive experiments on CIF AR-10, CIF AR-100, and CINIC-10 show that MFEF transfers more beneficial representational knowledge for distillation and outperforms alternative methods among various network architectures