DagRep.13.3.74.pdf
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Deep Continual Learning (Dagstuhl Seminar 23122)
Authors and all authors of the abstracts in this report
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Part of:
Issue:
Dagstuhl Reports, Volume 13, Issue 3
Volume: Dagstuhl Reports, Volume 13
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-10-16
Cite AsGet BibTex
Tinne Tuytelaars, Bing Liu, Vincenzo Lomonaco, Gido van de Ven, and Andrea Cossu. Deep Continual Learning (Dagstuhl Seminar 23122). In Dagstuhl Reports, Volume 13, Issue 3, pp. 74-91, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/DagRep.13.3.74
https://doi.org/10.4230/DagRep.13.3.74
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 23122 "Deep Continual Learning". This seminar brought together 26 researchers to discuss open problems and future directions of Continual Learning. The discussion revolved around key properties and the definition of Continual Learning itself, on the way Continual Learning should be evaluated, and on its real-world applications beyond academic research.
Subject Classification
ACM Subject Classification
- Computing methodologies → Learning settings
- Computing methodologies → Neural networks
Keywords
- continual learning
- incremental learning
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