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We study class-incremental learning, a training setup in which new classes of data are observed over time for the model to learn from.
Catastrophic forgetting, rehearsal and pseudorehearsal
Anthony Robins · 1992
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ImageNet: A large-scale hierarchical image database
Wei Dong, Richard Socher, Li Li-Jia, Kai Li, and Li Fei-Fei · 2009
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Ian J. Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2014
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Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 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
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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 S. Zemel · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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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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Fearnet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 2018
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Unsupervised learning using pretrained cnn and associative memory bank
Qun Liu and Supratik Mukhopadhyay · 2018
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Neural nearest neighbors networks
Tobias Plötz and Stefan Roth · 2018
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Memory replay gans: learning to generate images from new categories without forgetting
Chenshen Wu, Luis Herranz, Xialei Liu, Yaxing Wang, Joost van de Weijer, and Bogdan Raducanu · 2018
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Il2m: Class incremental learning with dual memory
Eden Belouadah and Adrian Popescu · 2019
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Continual learning with tiny episodic memories
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and M Ranzato · 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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Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
Memory-efficient incremental learning through feature adaptation
Ahmet Iscen, Jeffrey Zhang, Svetlana Lazebnik, and Cordelia Schmid · 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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Memory augmented neural model for incremental session-based recommendation
Fei Mi and Boi Faltings · 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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Google landmarks dataset v2-a large-scale benchmark for instance-level recognition and retrieval
Tobias Weyand, Andre Araujo, Bingyi Cao, and Jack Sim · 2020
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Ss-il: Separated softmax for incremental learning
Hongjoon Ahn, Jihwan Kwak, Subin Lim, Hyeonsu Bang, Hyojun Kim, and Taesup Moon · 2021
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Incremental learning using conditional adversarial networks
Ye Xiang, Ying Fu, Pan Ji, and Hua Huang · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Crosstransformers: spatially-aware few-shot transfer
Carl Doersch, Ankush Gupta, and Andrew Zisserman · 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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Entities as experts: Sparse memory access with entity supervision
Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald, Eunsol Choi, and Tom Kwiatkowski · 2020
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Attention-based query expansion learning
Albert Gordo, Filip Radenovic, and Tamara Berg · 2020
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Adaptive aggregation networks for class-incremental learning
Yaoyao Liu, Bernt Schiele, and Qianru Sun · 2021
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Essentials for class incremental learning
Sudhanshu Mittal, Silvio Galesso, and Thomas Brox · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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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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Der: Dynamically expandable representation for class incremental learning
Shipeng Yan, Jiangwei Xie, and Xuming He · 2021
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Lit: Zero-shot transfer with locked-image text tuning
Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer · 2021
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