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Exemplar-free class-incremental learning is very challenging due to the negative effect of catastrophic forgetting.
A case study of incremental concept induction
Jeffrey C Schlimmer and Douglas Fisher · 1986
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael Mccloskey and Neil J. Cohen · 1989
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The tradeoffs of large scale learning
Léon Bottou and Olivier Bousquet · 2007
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Scikit-learn: Machine learning in python
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Distance-based image classification: Generalizing to new classes at near-zero cost
Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka · 2013
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The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effects
M Mermillod, A Bugaiska, and P Bonin · 2013
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Cnn features off-the-shelf: an astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 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 S. Bernstein, Alexander C. Berg, and Fei-Fei Li · 2015
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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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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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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The extreme value machine
Ethan M Rudd, Lalit P Jain, Walter J Scheirer, and Terrance E Boult · 2017
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A strategy for an uncompromising incremental learner
Ragav Venkatesan, Hemanth Venkateswara, Sethuraman Panchanathan, and Baoxin Li · 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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Exemplar-supported generative reproduction for class incremental learning
Chen He, Ruiping Wang, Shiguang Shan, and Xilin Chen · 2018
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Measuring catastrophic forgetting in neural networks
Ronald Kemker, Marc McClure, Angelina Abitino, Tyler Hayes, and Christopher Kanan · 2018
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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What is being transferred in transfer learning?
Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang · 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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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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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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A survey on deep transfer learning
Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu · 2018
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A two-stage approach to few-shot learning for image recognition
Debasmit Das and CS George Lee · 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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Continual learning: A comparative study on how to defy forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory G. Slabaugh, and Tinne Tuytelaars · 2019
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Continual lifelong learning with neural networks: A review
German Ignacio Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, and Stefan Wermter · 2019
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Three scenarios for continual learning
Gido M Van de Ven and Andreas S Tolias · 2019
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A comprehensive study of class incremental learning algorithms for visual tasks
Eden Belouadah, Adrian Popescu, and Ioannis Kanellos · 2021
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Self-supervised features improve open-world learning
Akshay Raj Dhamija, Touqeer Ahmad, Jonathan Schwan, Mohsen Jafarzadeh, Chunchun Li, and Terrance E Boult · 2021
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Adaptive aggregation networks for class-incremental learning
Yaoyao Liu, Bernt Schiele, and Qianru Sun · 2021
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Class-incremental learning: survey and performance evaluation on image classification, 2021
Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D. Bagdanov, and Joost van de Weijer · 2021
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A tinyml platform for on-device continual learning with quantized latent replays
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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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Efficient feature transformations for discriminative and generative continual learning
Vinay Kumar Verma, Kevin J. Liang, Nikhil Mehta, Piyush Rai, and Lawrence Carin · 2021
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Class-incremental learning via dual augmentation
Fei Zhu, Zhen Cheng, Xu-yao Zhang, and Cheng-lin Liu · 2021
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Prototype augmentation and self-supervision for incremental learning
Fei Zhu, Xu-Yao Zhang, Chuang Wang, Fei Yin, and Cheng-Lin Liu · 2021
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Online continual learning for embedded devices
Tyler L Hayes and Christopher Kanan · 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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