Self-supervised Time Series Classification
Self-Supervised Time Series Classification is a methodological framework within time series classification that leverages self-supervised learning to extract discriminative representations from unlabeled time series data by designing pretext tasks that capture temporal dynamics, thereby enabling effective classification without relying on labeled training instances.
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2.9K
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223
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2017–2026
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Harbin University of Science and Technology
Harbin, China
4 h-index
14 publications
580 citations
Tsinghua University
Beijing, China
4 h-index
5 publications
189 citations
University of Oxford
Oxford, United Kingdom
3 h-index
16 publications
995 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
3 h-index
4 publications
325 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
3 h-index
3 publications
280 citations
Beijing Academy of Artificial Intelligence
Beijing, China
2 h-index
2 publications
42 citations

Shanghai Jiao Tong University
Shanghai, China
2 h-index
11 publications
289 citations
Nanyang Technological University
Singapore, Singapore
2 h-index
5 publications
749 citations
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Repository · 9 publications · 451 citations
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3 publications · 238 citations
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Conference · 2 publications · 72 citations
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TPAMI · 2 publications · 384 citations
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