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Disentangled Continual Learning: Separating Memory Edits from Model Updates: Related work
Authors:
(1) Sebastian Dziadzio, University of Tübingen ([email protected]);
(2) Çagatay Yıldız, University of Tübingen;
(3) Gido M. van de Ven, KU Leuven;
(4) Tomasz Trzcinski, IDEAS NCBR, Warsaw University of Technology, Tooploox;
(5) Tinne Tuytelaars, KU Leuven;
(6) Matthias Bethge, University of Tübingen.
Table of Links
2. Two problems with the current approach to class-incremental continual learning
3. Methods and 3.1. Infinite dSprites
4. Related work
4.1. Continual learning and 4.2. Benchmarking continual learning
5.1. Regularization methods and 5.2. Replay-based methods
5.4. One-shot generalization and 5.5. Open-set classification
Conclusion, Acknowledgments and References
This paper is available on arxiv under CC 4.0 license.
L O A D I N G
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