RSAC: Regularized subspace approximation classifier for lightweight continuous learning

Chih Hsing Ho, Shang Ho Tsai

研究成果: Conference contribution同行評審

摘要

Continuous learning seeks to perform the learning on the data that arrives from time to time. While prior works have demonstrated several possible solutions, these approaches require excessive training time as well as memory usage. This is impractical for applications where time and storage are constrained, such as edge computing. In this work, a novel training algorithm, regularized subspace approximation classifier (RSAC), is proposed to achieve lightweight continuous learning. RSAC contains a feature reduction module and classifier module with regularization. Extensive experiments show that RSAC is more efficient than prior continuous learning works and outperforms these works on various experimental settings.

原文English
主出版物標題Proceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
發行者Institute of Electrical and Electronics Engineers Inc.
頁面7985-7991
頁數7
ISBN(電子)9781728188089
DOIs
出版狀態Published - 2020
事件25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
持續時間: 10 1月 202115 1月 2021

出版系列

名字Proceedings - International Conference on Pattern Recognition
ISSN(列印)1051-4651

Conference

Conference25th International Conference on Pattern Recognition, ICPR 2020
國家/地區Italy
城市Virtual, Milan
期間10/01/2115/01/21

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