Incremental Learning
Incremental Learning is a machine learning paradigm in which a model progressively updates its knowledge by sequentially incorporating new data instances or tasks over time, without retraining from scratch, thereby maintaining computational efficiency and scalability in dynamic, large-scale data environments.
2.2K
Publications
134.2K
Citations
5.6K
Authors
1.5K
Institutions
Publications per year
2017–2026
1.1K
Variants
Sequential Learning
2.2K
Incremental Learning for Robust Visual Tracking
David A. Ross, Jongwoo Lim, Ruei-Sung Lin et al. · International Journal of Computer Vision · 2007 · 3.1K citations
1–5 of 2.2K
5.6K
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12
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1–5 of 5.6K
1.5K

Chinese Academy of Sciences
Beijing, China
31 h-index
136 publications
10.1K citations
Nanyang Technological University
Singapore, Singapore
24 h-index
63 publications
10.6K citations
University of Chinese Academy of Sciences
Beijing, China
24 h-index
82 publications
5.2K citations

University of Macau
Macao, Macao
23 h-index
41 publications
7.6K citations
Nanjing University
Nanjing, China
22 h-index
97 publications
7.3K citations

South China University of Technology
Guangzhou, China
22 h-index
56 publications
3K citations
Tsinghua University
Beijing, China
21 h-index
84 publications
5K citations

Shanghai Jiao Tong University
Shanghai, China
19 h-index
52 publications
2.3K citations
1–8 of 1.5K
42 publications · 6K citations
30
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Repository · 83 publications · 2.6K citations
27
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TPAMI · 25 publications · 3.6K citations
23
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34 publications · 1.2K citations
23
h-index
35 publications · 3.3K citations
21
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1–5