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Replay-based methods have proved their effectiveness on online continual learning by rehearsing past samples from an auxiliary memory.
On tiny episodic memories in continual learning
Chaudhry, A.; Rohrbach, M.; Elhoseiny, M.; Ajanthan, T.; Dokania, P. K.; Torr, P. H.; and Ranzato, M. 2019 · 1902
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Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, M.; and Cohen, N. J. 1989 · 1989
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CHILD: A first step towards continual learning
Ring, M. B. 1998 · 1998
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Lifelong learning algorithms
Thrun, S. 1998 · 1998
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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icarl: Incremental classifier and representation learning
Rebuffi, S.-A.; Kolesnikov, A.; Sperl, G.; and Lampert, C. H. 2017 · 2010
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Matching networks for one shot learning
Vinyals, O.; Blundell, C.; Lillicrap, T.; Wierstra, D.; et al. 2016 · 2016
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Expert gate: Lifelong learning with a network of experts
Aljundi, R.; Chakravarty, P.; and Tuytelaars, T. 2017 · 2017
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Pathnet: Evolution channels gradient descent in super neural networks
Fernando, C.; Banarse, D.; Blundell, C.; Zwols, Y.; Ha, D.; Rusu, A. A.; Pritzel, A.; and Wierstra, D. 2017 · 2017
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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 · 2017
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Overcoming catastrophic forgetting by incremental moment matching
Lee, S.-W.; Kim, J.-H.; Jun, J.; Ha, J.-W.; and Zhang, B.-T. 2017 · 2017
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Learning without forgetting
Li, Z.; and Hoiem, D. 2017 · 2017
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Gradient episodic memory for continual learning
Lopez-Paz, D.; and Ranzato, M. 2017 · 2017
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Continual learning with deep generative replay
Shin, H.; Lee, J. K.; Kim, J.; and Kim, J. 2017 · 2017
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Wang, T.; Zhu, J.-Y.; Torralba, A.; and Efros, A. A. 2018 · 2018
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Incremental learning using conditional adversarial networks
Xiang, Y.; Fu, Y.; Ji, P.; and Huang, H. 2019 · 2019
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Dark experience for general continual learning: a strong, simple baseline
Buzzega, P.; Boschini, M.; Porrello, A.; Abati, D.; and Calderara, S. 2020 · 2020
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Dataset Meta-Learning from Kernel Ridge-Regression
Nguyen, T.; Chen, Z.; and Lee, J. 2020 · 2020
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Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning
Mai, Z.; Li, R.; Kim, H.; and Sanner, S. 2021 · 2021
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Continual Normalization: Rethinking Batch Normalization for Online Continual Learning
Pham, Q.; Liu, C.; and Steven, H. 2022 · 2022
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Sample condensation in online continual learning
Sangermano, M.; Carta, A.; Cossu, A.; and Bacciu, D. 2022 · 2022
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Information-theoretic Online Memory Selection for Continual Learning
Sun, S.; Calandriello, D.; Hu, H.; Li, A.; and Titsias, M. 2022 · 2022
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Gcr: Gradient coreset based replay buffer selection for continual learning
Tiwari, R.; Killamsetty, K.; Iyer, R.; and Shenoy, P. 2022 · 2022
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Cafe: Learning to condense dataset by aligning features
Wang, K.; Zhao, B.; Peng, X.; Zhu, Z.; Yang, S.; Wang, S.; Huang, G.; Bilen, H.; Wang, X.; and You, Y. 2022 · 2022
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Online Coreset Selection for Rehearsal-based Continual Learning
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Nguyen, T.; Novak, R.; Xiao, L.; and Lee, J. 2021 · 2021
Cited alongside, same era.
Online class-incremental continual learning with adversarial shapley value
Shim, D.; Mai, Z.; Jeong, J.; Sanner, S.; Kim, H.; and Jang, J. 2021 · 2021
Cited alongside, same era.
Always be dreaming: A new approach for data-free class-incremental learning
Smith, J.; Hsu, Y.-C.; Balloch, J.; Shen, Y.; Jin, H.; and Kira, Z. 2021 · 2021
Cited alongside, same era.
Dataset Condensation with Gradient Matching
Zhao, B.; Mopuri, K. R.; and Bilen, H. 2021 · 2021
Cited alongside, same era.
Dataset distillation by matching training trajectories
Cazenavette, G.; Wang, T.; Torralba, A.; Efros, A. A.; and Zhu, J.-Y. 2022 · 2022
Cited alongside, same era.
Not Just Selection, but Exploration: Online Class-Incremental Continual Learning via Dual View Consistency
Gu, Y.; Yang, X.; Wei, K.; and Deng, C. 2022 · 2022
Cited alongside, same era.
Online continual learning through mutual information maximization
Guo, Y.; Liu, B.; and Zhao, D. 2022 · 2022
Cited alongside, same era.
Yoon, J.; Madaan, D.; Yang, E.; and Hwang, S. J. 2022 · 2022
Later among the works it cites.
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal
Zhang, Y.; Pfahringer, B.; Frank, E.; Bifet, A.; Lim, N. J. S.; and Jia, A. 2022 · 2022
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Minimizing the accumulated trajectory error to improve dataset distillation
Du, J.; Jiang, Y.; Tan, V. Y.; Zhou, J. T.; and Li, H. 2023 · 2023
Closest in time.
Real-time evaluation in online continual learning: A new hope
Ghunaim, Y.; Bibi, A.; Alhamoud, K.; Alfarra, M.; Al Kader Hammoud, H. A.; Prabhu, A.; Torr, P. H.; and Ghanem, B. 2023 · 2023
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Efficient Dataset Distillation via Minimax Diffusion
Gu, J.; Vahidian, S.; Kungurtsev, V.; Wang, H.; Jiang, W.; You, Y.; and Chen, Y. 2023 · 2023
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SIESTA: Efficient Online Continual Learning with Sleep
Harun, M. Y.; Gallardo, J.; Hayes, T. L.; Kemker, R.; and Kanan, C. 2023 · 2023
Closest in time.
Can pre-trained models assist in dataset distillation?
Lu, Y.; Chen, X.; Zhang, Y.; Gu, J.; Zhang, T.; Zhang, Y.; Yang, X.; Xuan, Q.; Wang, K.; and You, Y. 2023 · 2023
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Online continual learning without the storage constraint
Prabhu, A.; Cai, Z.; Dokania, P.; Torr, P.; Koltun, V.; and Sener, O. 2023 · 2023
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DiM: Distilling Dataset into Generative Model
Wang, K.; Gu, J.; Zhou, D.; Zhu, Z.; Jiang, W.; and You, Y. 2023 · 2023
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Dataset condensation with distribution matching
Zhao, B.; and Bilen, H. 2023 · 2023
Closest in time.