Domain-incremental Learning
Domain-Incremental Learning is a continual learning paradigm where the model incrementally adapts to new data distributions from distinct domains while preserving knowledge from previous domains, with the assumption that the task identity is known during inference.
119
Publications
7.6K
Citations
474
Authors
182
Institutions
Publications per year
2017–2026
95
Variants
Domain-incremental Continual Learning
Parents
119
Learning in the Presence of Concept Drift and Hidden Contexts
Gerhard Widmer, Miroslav Kubát · Machine Learning · 1996 · 1.4K citations · Full text
Learning in the presence of concept drift and hidden contexts
Gerhard Widmer, Miroslav Kubát · Machine Learning · 1996 · 993 citations · Full text
1–5 of 119
474
4
h-index
3
h-index
3
h-index
3
h-index
3
h-index
1–5 of 474
182
Google (United States)
Mountain View, United States
4 h-index
7 publications
225 citations

Chinese Academy of Sciences
Beijing, China
4 h-index
7 publications
162 citations
University of Cambridge
Cambridge, United Kingdom
4 h-index
5 publications
738 citations
University of Chinese Academy of Sciences
Beijing, China
4 h-index
9 publications
245 citations
Tsinghua University
Beijing, China
3 h-index
8 publications
313 citations
Rochester Institute of Technology
Rochester, United States
3 h-index
9 publications
144 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
3 h-index
5 publications
741 citations
University of Illinois Chicago
Chicago, United States
3 h-index
7 publications
320 citations
1–8 of 182
Repository · 17 publications · 425 citations
13
h-index
Conference · 6 publications · 229 citations
6
h-index
Conference · 4 publications · 222 citations
4
h-index
3 publications · 108 citations
3
h-index
Conference · 3 publications · 99 citations
3
h-index
1–5