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Continual Learning (CL) sequentially learns new tasks like human beings, with the goal to achieve better Stability (S, remembering past tasks) and Plasticity (P, adapting to new tasks).
Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions
Roger Ratcliff · 1990
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Design and regularization of neural networks: the optimal use of a validation set
Jan Larsen, Lars Kai Hansen, Claus Svarer, and M Ohlsson · 1996
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Steepest descent methods for multicriteria optimization
Jörg Fliege and Benar Fux Svaiter · 2000
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Searching for robust pareto-optimal solutions in multi-objective optimization
Kalyanmoy Deb and Himanshu Gupta · 2005
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Multiple-gradient descent algorithm (mgda) for multiobjective optimization
Jean-Antoine Désidéri · 2012
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Scalable gradient-based tuning of continuous regularization hyperparameters
Jelena Luketina, Mathias Berglund, Klaus Greff, and Tapani Raiko · 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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Pathnet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A Rusu, Alexander Pritzel, and Daan Wierstra · 2017
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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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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
Cited alongside, same era.
Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2018
Cited alongside, same era.
Packnet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
Cited alongside, same era.
Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
Cited alongside, same era.
Learning to learn without forgetting by maximizing transfer and minimizing interference
Learning to reweight with deep interactions
Yang Fan, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Jiang Bian, Tao Qin, and Xiang-Yang Li · 2020
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Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2020
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Optimizing millions of hyperparameters by implicit differentiation
Jonathan Lorraine, Paul Vicol, and David Duvenaud · 2020
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Interpretable Machine Learning
Christoph Molnar · 2020
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Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip Torr, and Puneet Dokania · 2020
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Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2018
Cited alongside, same era.
Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
Cited alongside, same era.
Data dropout: Optimizing training data for convolutional neural networks
Tianyang Wang, Jun Huan, and Bo Li · 2018
Cited alongside, same era.
Online continual learning with maximal interfered retrieval
Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, and Lucas Page-Caccia · 2019
Cited alongside, same era.
Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 2019
Cited alongside, same era.
On tiny episodic memories in continual learning
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc’Aurelio Ranzato · 2019
Cited alongside, same era.
Learning with long-term remembering: Following the lead of mixed stochastic gradient
Yunhui Guo, Mingrui Liu, Tianbao Yang, and Tajana Rosing · 2019
Cited alongside, same era.
An investigation of replay-based approaches for continual learning
Benedikt Bagus and Alexander Gepperth · 2021
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Rethinking experience replay: a bag of tricks for continual learning
Pietro Buzzega, Matteo Boschini, Angelo Porrello, and Simone Calderara · 2021
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Using hindsight to anchor past knowledge in continual learning
Arslan Chaudhry, Albert Gordo, Puneet Dokania, Philip Torr, and David Lopez-Paz · 2021
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Multi-domain multi-task rehearsal for lifelong learning
Fan Lyu, Shuai Wang, Wei Feng, Zihan Ye, Fuyuan Hu, and Song Wang · 2021
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Gradient-based editing of memory examples for online task-free continual learning
Liu Risheng, Liu Yaohua, Zeng Shangzhi, and Zhang Jin · 2021
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Class-incremental lifelong learning in multi-label classification
Kaile Du, Linyan Li, Fan Lyu, Fuyuan Hu, Zhenping Xia, and Fenglei Xu · 2022
Closest in time.
Agcn: Augmented graph convolutional network for lifelong multi-label image recognition
Kaile Du, Fan Lyu, Fuyuan Hu, Linyan Li, Wei Feng, Fenglei Xu, and Qiming Fu · 2022
Closest in time.