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The goal of continual learning is to improve the performance of recognition models in learning sequentially arrived data.
Three scenarios for continual learning
Gido M van de Ven and Andreas S Tolias · 1904
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Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory
James L McClelland, Bruce L McNaughton, and Randall C O’Reilly · 1995
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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
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, et al · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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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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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, et al · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Incremental learning of object detectors without catastrophic forgetting
Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, et al · 2018
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Overcoming catastrophic forgetting with hard attention to the task
Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
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Learning without memorizing
Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng, et al · 2019
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Spottune: transfer learning through adaptive fine-tuning
Yunhui Guo, Honghui Shi, Abhishek Kumar, et al · 2019
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Rethinking imagenet pre-training
Kaiming He, Ross Girshick, and Piotr Dollár · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 2019
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Moment matching for multi-source domain adaptation
Xingchao Peng et al · 2019
Cited alongside, same era.
Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, et al · 2019
Cited alongside, same era.
Dark experience for general continual learning: a strong, simple baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, et al · 2020
Cited alongside, same era.
The turking test: Can language models understand instructions?
Avia Efrat and Omer Levy · 2020
Cited alongside, same era.
Remind your neural network to prevent catastrophic forgetting
Tyler L Hayes, Kushal Kafle, Robik Shrestha, et al · 2020
Cited alongside, same era.
Lifelong machine learning with deep streaming linear discriminant analysis
Tyler L Hayes and Christopher Kanan · 2020
Afec: Active forgetting of negative transfer in continual learning
Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, and Yi Zhong · 2021
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Memory replay with data compression for continual learning
Liyuan Wang, Xingxing Zhang, Kuo Yang, Longhui Yu, Chongxuan Li, Lanqing Hong, Shifeng Zhang, Zhenguo Li, Yi Zhong, and Jun Zhu · 2021
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Few-shot segmentation via cycle-consistent transformer
Gengwei Zhang, Guoliang Kang, Yi Yang, and Yunchao Wei · 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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Continual pre-training mitigates forgetting in language and vision
Andrea Cossu, Tinne Tuytelaars, Antonio Carta, Lucia Passaro, Vincenzo Lomonaco, and Davide Bacciu · 2022
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Cited alongside, same era.
Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip HS Torr, and Puneet K Dokania · 2020
Cited alongside, same era.
Revisiting few-sample bert fine-tuning
Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q Weinberger, and Yoav Artzi · 2020
Cited alongside, same era.
An empirical study of training self-supervised vision transformers
Xinlei Chen, Saining Xie, and Kaiming He · 2021
Cited alongside, same era.
The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, et al · 2021
Cited alongside, same era.
How well self-supervised pre-training performs with streaming data?
Dapeng Hu, Qizhengqiu Lu, Lanqing Hong, Hailin Hu, Yifan Zhang, Zhenguo Li, Alfred Shen, and Jiashi Feng · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Cited alongside, same era.
Self-supervised models are continual learners
Enrico Fini, Victor G Turrisi da Costa, Xavier Alameda-Pineda, et al · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, et al · 2022
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Continual learning of natural language processing tasks: A survey
Zixuan Ke and Bing Liu · 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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Dualprompt: Complementary prompting for rehearsal-free continual learning
Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, et al · 2022
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Learning to prompt for continual learning
Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, et al · 2022
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Mitigating neural network overconfidence with logit normalization
Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li · 2022
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Class-incremental learning with strong pre-trained models
Tz-Ying Wu, Gurumurthy Swaminathan, Zhizhong Li, et al · 2022
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Continual object detection via prototypical task correlation guided gating mechanism
Binbin Yang, Xinchi Deng, Han Shi, Changlin Li, Gengwei Zhang, Hang Xu, Shen Zhao, Liang Lin, and Xiaodan Liang · 2022
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Mining unseen classes via regional objectness: A simple baseline for incremental segmentation
Zekang Zhang, Guangyu Gao, Zhiyuan Fang, Jianbo Jiao, and Yunchao Wei · 2022
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A comprehensive survey of continual learning: Theory, method and application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu · 2023
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Coinseg: Contrast inter- and intra- class representations for incremental segmentation
Zekang Zhang, Guangyu Gao, Jianbo Jiao, Chi Harold Liu, and Yunchao Wei · 2023
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Ctp: Towards vision-language continual pretraining via compatible momentum contrast and topology preservation, 2023
Hongguang Zhu, Yunchao Wei, Xiaodan Liang, Chunjie Zhang, and Yao Zhao · 2023
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