Self-supervised Knowledge Distillation Using Singular Value Decomposition
Seung Hyun Lee, Dae Ha Kim, Byung Cheol Song
Abstract
To solve deep neural network (DNN)'s huge training dataset and its high computation issue, so-called teacher-student (T-S) DNN which transfers the knowledge of T-DNN to S-DNN has been proposed. However, the existing T-S-DNN has limited range of use, and the knowledge of T-DNN is insufficiently transferred to S-DNN. To improve the quality of the transferred knowledge from T-DNN, we propose a new knowledge distillation using singular value decomposition (SVD). In addition, we define a knowledge transfer as a self-supervised task and suggest a way to continuously receive information from T-DNN. Simulation results show that a S-DNN with a computational cost of 1/5 of the T-DNN can be up to 1.1% better than the T-DNN in terms of classification accuracy. Also assuming the same computational cost, our S-DNN outperforms the S-DNN driven by the state-of-the-art distillation with a performance advantage of 1.79%. code is available on https://github.com/sseung0703/SSKD_SVD.
BibTeX
@inproceedings{eccv2018_selfsupervisedkn,
title = {Self-supervised Knowledge Distillation Using Singular Value Decomposition},
author = {Seung Hyun Lee and Dae Ha Kim and Byung Cheol Song},
booktitle = {ECCV 2018},
year = {2018}
}