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Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Natural gradient works efficiently in learning
Shun-Ichi Amari · 1998
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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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Learning multiple layers of features from tiny images
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Training deep and recurrent networks with hessian-free optimization
James Martens and Ilya Sutskever · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2015
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Less-forgetting learning in deep neural networks
Heechul Jung, Jeongwoo Ju, Minju Jung, and Junmo Kim · 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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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Gradient episodic memory for continual learning
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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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Tiny imagenet challenge
Jiayu Wu, Qixiang Zhang, and Guoxi Xu · 2017
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Deesil: Deep-shallow incremental learning
Eden Belouadah and Adrian Popescu · 2018
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End-to-end incremental learning
Francisco M Castro, Manuel J Marín-Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari · 2018
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Rotate your networks: Better weight consolidation and less catastrophic forgetting
Xialei Liu, Marc Masana, Luis Herranz, Joost Van de Weijer, Antonio M Lopez, and Andrew D Bagdanov · 2018
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Online structured laplace approximations for overcoming catastrophic forgetting
Hippolyt Ritter, Aleksandar Botev, and David Barber · 2018
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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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Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
Always be dreaming: A new approach for data-free class-incremental learning
James Smith, Yen-Chang Hsu, Jonathan Balloch, Yilin Shen, Hongxia Jin, and Zsolt Kira · 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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Model-centric data manifold: the data through the eyes of the model
Luca Grementieri and Rita Fioresi · 2022
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Class-incremental learning by knowledge distillation with adaptive feature consolidation
Minsoo Kang, Jaeyoo Park, and Bohyung Han · 2022
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Online continual learning in image classification: An empirical survey
Zheda Mai, Ruiwen Li, Jihwan Jeong, David Quispe, Hyunwoo Kim, and Scott Sanner · 2022
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Class-incremental learning: survey and performance evaluation on image classification
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On warm-starting neural network training
Jordan Ash and Ryan P Adams · 2020
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Podnet: Pooled outputs distillation for small-tasks incremental learning
Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, and Eduardo Valle · 2020
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New insights and perspectives on the natural gradient method
James Martens · 2020
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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
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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 · 2021
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One pass imagenet
Huiyi Hu, Ang Li, Daniele Calandriello, and Dilan Gorur · 2021
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Marc Masana, Xialei Liu, Bartłomiej Twardowski, Mikel Menta, Andrew D Bagdanov, and Joost Van De Weijer · 2022
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Bring evanescent representations to life in lifelong class incremental learning
Marco Toldo and Mete Ozay · 2022
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A model or 603 exemplars: Towards memory-efficient class-incremental learning
Da-Wei Zhou, Qi-Wei Wang, Han-Jia Ye, and De-Chuan Zhan · 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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First session adaptation: A strong replay-free baseline for class-incremental learning
Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino, Rahaf Aljundi, and Richard E. Turner · 2023
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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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A comprehensive empirical evaluation on online continual learning
Albin Soutif-Cormerais, Antonio Carta, Andrea Cossu, Julio Hurtado, Vincenzo Lomonaco, Joost Van de Weijer, and Hamed Hemati · 2023
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Deep class-incremental learning: A survey
Da-Wei Zhou, Qi-Wei Wang, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan, and Ziwei Liu · 2023
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