The DOI matches its Crossref record; 29 of the 33 references it cites were found in a registry or an index.
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]
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“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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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611835114 · Cited by 7,899
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
iCaRL: Incremental Classifier and Representation Learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., & Lampert, C. H. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.587 · Cited by 3,988
Natural Gradient Works Efficiently in Learning
Амари, Ш.-И. (1998) · Neural Computation · doi:10.1162/089976698300017746 · Cited by 2,914
Theory of the backpropagation neural network
Hecht-Nielsen (1989) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1989.118638 · Cited by 2,274
Research works printed but not linked: 1
- Le Roux, N., Pierre-Antoine, M., Bengio, Y.: Topmoumoute online natural gradient algorithm. In: NIPS (2007)
Cited by
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A Continual Learning Survey: Defying Forgetting in Classification Tasks
Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., & Tuytelaars, T. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3057446 · Cited by 1,647
A Comprehensive Survey of Continual Learning: Theory, Method and Application
Wang, L., Zhang, X., Su, H., & Zhu, J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3367329 · Cited by 1,113
“Besides, weight regularization can also be combined with experience replay for better performance and generality [44], [52].”
BackgroundMethodInfluentialEfficient Lifelong Learning with A-GEM
Chaudhry, A., Ranzato, M., Rohrbach, M., & Elhoseiny, M. (2018) · arXiv · doi:10.48550/arxiv.1812.00420 · Cited by 852
Self-citationLearning to Prompt for Continual Learning
Wang, Z., Zhang, Z., Lee, C., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., & Pfister, T. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00024 · Cited by 936
DER: Dynamically Expandable Representation for Class Incremental Learning
Yan, S., Xie, J., & He, X. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00303 · Cited by 803
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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