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Self-Bidirectional Decoupled Distillation for Time Series Classification

Xiao, Zhiwen; Xing, Huanlai; Qu, Rong; Li, Hui; Feng, Li; Zhao, Bowen; Yang, Jiayi

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Authors

Zhiwen Xiao

Huanlai Xing

Hui Li

Li Feng

Bowen Zhao

Jiayi Yang



Abstract

Over the years, many deep learning algorithms have been developed for time series classification (TSC). A learning model’s performance usually depends on the quality of the semantic information extracted from lower and higher levels within the representation hierarchy. Efficiently promoting mutual learning between higher and lower levels is vital to enhance the model’s performance during model learning. To this end, we propose a self-bidirectional decoupled distillation (Self-BiDecKD) method for TSC. Unlike most self-distillation algorithms that usually transfer the target-class knowledge from higher to lower levels, Self-BiDecKD encourages the output of the output layer and the output of each lower-level block to form a bidirectional decoupled knowledge distillation (KD) pair. The bidirectional decoupled KD promotes mutual learning between lower- and higher-level semantic information and extracts the knowledge hidden in the target and non-target classes, helping Self-BiDecKD capture rich representations from the data. Experimental results show that compared with a number of self-distillation algorithms, Self-BiDecKD wins 35 out of 85 UCR2018 datasets and achieves the smallest AVG_rank score, namely 3.2882. In particular, compared with a non-self-distillation Baseline, Self-BiDecKD results in 58/8/19 regarding ‘win’/‘tie’/‘lose’.

Citation

Xiao, Z., Xing, H., Qu, R., Li, H., Feng, L., Zhao, B., & Yang, J. (2024). Self-Bidirectional Decoupled Distillation for Time Series Classification. IEEE Transactions on Artificial Intelligence, https://doi.org/10.1109/tai.2024.3360180

Journal Article Type Article
Acceptance Date Jan 28, 2024
Publication Date Feb 11, 2024
Deposit Date Apr 2, 2024
Publicly Available Date Apr 8, 2024
Journal IEEE Transactions on Artificial Intelligence
Electronic ISSN 2691-4581
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/tai.2024.3360180
Keywords Feature extraction , Semantics , Time series analysis , Data mining , Brain modeling , Deep learning , Heuristic algorithms , Convolutional Neural Network , Deep Learning , Data Mining , Knowledge Distillation , Time Series Classification
Public URL https://nottingham-repository.worktribe.com/output/31435533
Additional Information ©2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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