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Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phase-by-phase.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions
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Catastrophic interference is eliminated in pre-trained networks
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The nonstochastic multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire · 2002
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Experts in a markov decision process
Eyal Even-Dar, Sham M Kakade, and Yishay Mansour · 2005
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The theory and practice of online learning
Terry Anderson · 2008
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Online markov decision processes
Eyal Even-Dar, Sham M Kakade, and Yishay Mansour · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Ucb revisited: Improved regret bounds for the stochastic multi-armed bandit problem
Peter Auer and Ronald Ortner · 2010
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Analysis of thompson sampling for the multi-armed bandit problem
Shipra Agrawal and Navin Goyal · 2012
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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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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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Progressive neural networks
Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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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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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Encoder based lifelong learning
Amal Rannen Triki, Rahaf Aljundi, Matthew B. Blaschko, and Tinne Tuytelaars · 2017
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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Mnemonics training: Multi-class incremental learning without forgetting
Yaoyao Liu, Yuting Su, An-An Liu, Bernt Schiele, and Qianru Sun · 2020
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Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip HS Torr, and Puneet K Dokania · 2020
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Topology-preserving class-incremental learning
Xiaoyu Tao, Xinyuan Chang, Xiaopeng Hong, Xing Wei, and Yihong Gong · 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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Distilling causal effect of data in class-incremental learning
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Automatic differentiation in machine learning: a survey
Atilim Gunes Baydin, Barak A Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2018
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Bilevel programming for hyperparameter optimization and meta-learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil · 2018
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Reinforced continual learning
Ju Xu and Zhanxing Zhu · 2018
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Il2m: Class incremental learning with dual memory
Eden Belouadah and Adrian Popescu · 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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Online learning for markov decision processes in nonstationary environments: A dynamic regret analysis
Yingying Li and Na Li · 2019
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Xinting Hu, Kaihua Tang, Chunyan Miao, Xian-Sheng Hua, and Hanwang Zhang · 2021
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Online optimal control with affine constraints
Yingying Li, Subhro Das, and Na Li · 2021
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Adaptive aggregation networks for class-incremental learning
Yaoyao Liu, Bernt Schiele, and Qianru Sun · 2021
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Rmm: Reinforced memory management for class-incremental learning
Yaoyao Liu, Bernt Schiele, and Qianru Sun · 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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Energy-based latent aligner for incremental learning
K. J. Joseph, Salman Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, and Vineeth N Balasubramanian · 2022
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Looking back on learned experiences for class/task incremental learning
Mozhgan PourKeshavarzi, Guoying Zhao, and Mohammad Sabokrou · 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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