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Continual learning entails learning a sequence of tasks and balancing their knowledge appropriately.
Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
James L McClelland, Bruce L McNaughton, and Randall C O’Reilly · 1995
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
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Understanding batch normalization
Nils Bjorck, Carla P Gomes, Bart Selman, and Kilian Q Weinberger · 2018
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How does batch normalization help optimization?
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry · 2018
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Evidential deep learning to quantify classification uncertainty
Murat Sensoy, Lance Kaplan, and Melih Kandemir · 2018
Earlier work this paper cites.
Three continual learning scenarios
Gido M van de Ven and Andreas S Tolias · 2018
Cited alongside, same era.
Group normalization
Yuxin Wu and Kaiming He · 2018
Cited alongside, same era.
Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2019
Cited alongside, same era.
Towards understanding regularization in batch normalization
Ping Luo, Xinjiang Wang, Wenqi Shao, and Zhanglin Peng · 2019
Cited alongside, same era.
Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
Cited alongside, same era.
Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
Cited alongside, same era.
Afec: Active forgetting of negative transfer in continual learning
Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, and Yi Zhong · 2021
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On the effectiveness of lipschitz-driven rehearsal in continual learning
Lorenzo Bonicelli, Matteo Boschini, Angelo Porrello, Concetto Spampinato, and Simone Calderara · 2022
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Class-incremental continual learning into the extended der-verse
Matteo Boschini, Lorenzo Bonicelli, Pietro Buzzega, Angelo Porrello, and Simone Calderara · 2022
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New insights on reducing abrupt representation change in online continual learning
Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, and Eugene Belilovsky · 2022
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Continual normalization: Rethinking batch normalization for online continual learning
Quang Pham, Chenghao Liu, and Hoi Steven · 2022
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Dark experience for general continual learning: a strong, simple baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara · 2020
Cited alongside, same era.
Embracing change: Continual learning in deep neural networks
Raia Hadsell, Dushyant Rao, Andrei A Rusu, and Razvan Pascanu · 2020
Cited alongside, same era.
Being bayesian about categorical probability
Taejong Joo, Uijung Chung, and Min-Gwan Seo · 2020
Cited alongside, same era.
Gradient projection memory for continual learning
Gobinda Saha, Isha Garg, and Kaushik Roy · 2021
Cited alongside, same era.
A correspondence between normalization strategies in artificial and biological neural networks
Yang Shen, Julia Wang, and Saket Navlakha · 2021
Cited alongside, same era.
Coscl: Cooperation of small continual learners is stronger than a big one
Liyuan Wang, Xingxing Zhang, Qian Li, Jun Zhu, and Yi Zhong · 2022
Later among the works it cites.
Memory replay with data compression for continual learning
Liyuan Wang, Xingxing Zhang, Kuo Yang, Longhui Yu, Chongxuan Li, Lanqing Hong, Shifeng Zhang, Zhenguo Li, Yi Zhong, and Jun Zhu · 2022
Later among the works it cites.
Diagnosing batch normalization in class incremental learning
Minghao Zhou, Quanziang Wang, Jun Shu, Qian Zhao, and Deyu Meng · 2022
Later among the works it cites.
Rebalancing batch normalization for exemplar-based class-incremental learning
Sungmin Cha, Sungjun Cho, Dasol Hwang, Sunwon Hong, Moontae Lee, and Taesup Moon · 2023
Closest in time.
Incorporating neuro-inspired adaptability for continual learning in artificial intelligence
Liyuan Wang, Xingxing Zhang, Qian Li, Mingtian Zhang, Hang Su, Jun Zhu, and Yi Zhong · 2023
Closest in time.
A comprehensive survey of continual learning: Theory, method and application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu · 2023
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