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Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
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“Rwalk improves the regularization methods of EWC and SI, and further improves the performance based on them. However, for performance reasons, some old data are still retained. This shows the effectiveness of rehearsal strategies for mitigating catastrophic forgetting.”
From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [47]
In the citing paper
“Many incremental learning methods based on the regularization strategy save a small amount of old data to enhance model performance [47,70,76].”
From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [47]
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“The experimental results of [47,70,76] prove that the algorithm based on the regularization strategy is appropriately combined with a rehearsal or pseudo-rehearsal strategy, which is of great help to improve the performance of the model.”
From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [47]
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Metadata
- Authors
- Chaudhry, A., Dokania, P. K., Ajanthan, T., & Torr, P. H. S.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Ajanthan, Thalaiyasingam
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 556-572
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2786446225
- Cited by
- 1,091
- References
- 33
- Citations by year
- 2026: 78 · 2025: 182 · 2024: 217 · 2023: 189 · 2022: 135 · 2021: 154 · 2020: 84 · 2019: 45 · 2018: 7
- FWCI
- 29.59
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council (EP/N019474/1, EP/M013774/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- incremental learning, catastrophic forgetting, intransigence, path integral, Kullback-Leibler divergence, knowledge preservation, classifier updating
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