A Novel Self-Knowledge Distillation Approach with Siamese Representation Learning for Action Recognition

Duc Quang Vu, Thi Thu Trang Phung, Jia Ching Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Knowledge distillation is an effective transfer of knowledge from a heavy network (teacher) to a small network (student) to boost students' performance. Self-knowledge dis-tillation, the special case of knowledge distillation, has been proposed to remove the large teacher network training process while preserving the student's performance. This paper intro-duces a novel Self-knowledge distillation approach via Siamese representation learning, which minimizes the difference between two representation vectors of the two different views from a given sample. Our proposed method, SKD-SRL, utilizes both soft label distillation and the similarity of representation vectors. Therefore, SKD-SRL can generate more consistent predictions and representations in various views of the same data point. Our benchmark has been evaluated on various standard datasets. The experimental results have shown that SKD-SRL significantly improves the accuracy compared to existing supervised learning and knowledge distillation methods regardless of the networks.

Original languageEnglish
Title of host publication2021 International Conference on Visual Communications and Image Processing, VCIP 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728185514
DOIs
StatePublished - 2021
Event2021 International Conference on Visual Communications and Image Processing, VCIP 2021 - Munich, Germany
Duration: 5 Dec 20218 Dec 2021

Publication series

Name2021 International Conference on Visual Communications and Image Processing, VCIP 2021 - Proceedings

Conference

Conference2021 International Conference on Visual Communications and Image Processing, VCIP 2021
Country/TerritoryGermany
CityMunich
Period5/12/218/12/21

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