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Class-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Catastrophic forgetting in connectionist networks
Robert M French · 1999
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2016
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Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Expert gate: Lifelong learning with a network of experts
Rahaf Aljundi, Punarjay Chakravarty, and Tinne Tuytelaars · 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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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H. Lampert · 2017
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Deesil: Deep-shallow incremental learning
Eden Belouadah and Adrian Popescu · 2018
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet Kumar Dokania, Thalaiyasingam Ajanthan, and Philip H. S. Torr · 2018
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Overcoming catastrophic forgetting with hard attention to the task
Joan Serrà, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
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Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2019
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Significance of softmax-based features in comparison to distance metric learning-based features
Shota Horiguchi, Daiki Ikami, and Kiyoharu Aizawa · 2019
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Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Continual unsupervised representation learning
Dushyant Rao, Francesco Visin, Andrei A. Rusu, Razvan Pascanu, Yee Whye Teh, and Raia Hadsell · 2019
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A tinyml platform for on-device continual learning with quantized latent replays
Leonardo Ravaglia, Manuele Rusci, Davide Nadalini, Alessandro Capotondi, Francesco Conti, and Luca Benini · 2021
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Always be dreaming: A new approach for data-free class-incremental learning
James Seale Smith, Yen-Chang Hsu, Jonathan Balloch, Yilin Shen, Hongxia Jin, and Zsolt Kira · 2021
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Class-incremental learning with generative classifiers
Gido M. van de Ven, Zhe Li, and Andreas S. Tolias · 2021
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DER: dynamically expandable representation for class incremental learning
Shipeng Yan, Jiangwei Xie, and Xuming He · 2021
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Continual learning with bayesian model based on a fixed pre-trained feature extractor
Yang Yang, Zhiying Cui, Junjie Xu, Changhong Zhong, Ruixuan Wang, and Wei-Shi Zheng · 2021
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Gido M Van de Ven and Andreas S Tolias · 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.
More classifiers, less forgetting: A generic multi-classifier paradigm for incremental learning
Yu Liu, Sarah Parisot, Gregory Slabaugh, Xu Jia, Ales Leonardis, and Tinne Tuytelaars · 2020
Cited alongside, same era.
Efficient continual learning with modular networks and task-driven priors
Tom Veniat, Ludovic Denoyer, and Marc’Aurelio Ranzato · 2020
Cited alongside, same era.
Semantic drift compensation for class-incremental learning
Lu Yu, Bartlomiej Twardowski, Xialei Liu, Luis Herranz, Kai Wang, Yongmei Cheng, Shangling Jui, and Joost van de Weijer · 2020
Cited alongside, same era.
Maintaining discrimination and fairness in class incremental learning
Bowen Zhao, Xi Xiao, Guojun Gan, Bin Zhang, and Shu-Tao Xia · 2020
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Optimizing reusable knowledge for continual learning via metalearning
Julio Hurtado, Alain Raymond, and Alvaro Soto · 2021
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Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, and De-Chuan Zhan · 2021
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Continual learning beyond a single model
Thang Doan, Seyed Iman Mirzadeh, and Mehrdad Farajtabar · 2022
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Class-incremental learning: Survey and performance evaluation on image classification
Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D Bagdanov, and Joost van de Weijer · 2022
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FOSTER: feature boosting and compression for class-incremental learning
Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2022
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Coscl: Cooperation of small continual learners is stronger than a big one
Liyuan Wang, Xingxing Zhang, Qian Li, Jun Zhu, and Yi Zhong · 2022
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Self-sustaining representation expansion for non-exemplar class-incremental learning
Kai Zhu, Wei Zhai, Yang Cao, Jiebo Luo, and Zheng-Jun Zha · 2022
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Fetril: Feature translation for exemplar-free class-incremental learning
Grégoire Petit, Adrian Popescu, Hugo Schindler, David Picard, and Bertrand Delezoide · 2023
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Weighted ensemble self-supervised learning
Yangjun Ruan, Saurabh Singh, Warren R. Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, and Joshua V. Dillon · 2023
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