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Prompt-based continual learning is an emerging direction in leveraging pre-trained knowledge for downstream continual learning, and has almost reached the performance pinnacle under supervised pre-training.
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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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Notmnist dataset
Yaroslav Bulatov · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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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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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 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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Three scenarios for continual learning
Gido M Van de Ven and Andreas S Tolias · 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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Adversarial continual learning
Sayna Ebrahimi, Franziska Meier, Roberto Calandra, Trevor Darrell, and Marcus Rohrbach · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 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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An empirical study of training self-supervised vision transformers
Xinlei Chen, Saining Xie, and Kaiming He · 2021
Cited alongside, same era.
How well does self-supervised pre-training perform with streaming data?
Dapeng Hu, Shipeng Yan, Qizhengqiu Lu, HONG Lanqing, Hailin Hu, Yifan Zhang, Zhenguo Li, Xinchao Wang, 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.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
Representational continuity for unsupervised continual learning
Divyam Madaan, Jaehong Yoon, Yuanchun Li, Yunxin Liu, and Sung Ju Hwang · 2021
Cited alongside, same era.
A theoretical study on solving continual learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, Zixuan Ke, and Bing Liu · 2022
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Adult newborn granule cells confer emotional state–dependent adaptability in memory retrieval
Bo Lei, Bilin Kang, Wantong Lin, Haichao Chen, Yuejun Hao, Jian Ma, Songhai Shi, and Yi Zhong · 2022
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Social experiences switch states of memory engrams through regulating hippocampal rac1 activity
Bo Lei, Li Lv, Shiqiang Hu, Yikai Tang, and Yi Zhong · 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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S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning
Yabin Wang, Zhiwu Huang, and Xiaopeng Hong · 2022
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Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, and Emma Strubell · 2021
Cited alongside, same era.
Effect of scale on catastrophic forgetting in neural networks
Vinay Venkatesh Ramasesh, Aitor Lewkowycz, and Ethan Dyer · 2021
Cited alongside, same era.
Imagenet-21k pretraining for the masses
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor · 2021
Cited alongside, same era.
Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning
Liyuan Wang, Kuo Yang, Chongxuan Li, Lanqing Hong, Zhenguo Li, and Jun Zhu · 2021
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Image bert pre-training with online tokenizer
Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong · 2021
Cited alongside, same era.
Dualprompt: Complementary prompting for rehearsal-free continual learning
Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, et al · 2022
Later among the works it cites.
Learning to prompt for continual learning
Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister · 2022
Later among the works it cites.
Promptfusion: Decoupling stability and plasticity for continual learning
Haoran Chen, Zuxuan Wu, Xintong Han, Menglin Jia, and Yu-Gang Jiang · 2023
Closest in time.
Steering prototype with prompt-tuning for rehearsal-free continual learning
Zhuowei Li, Long Zhao, Zizhao Zhang, Han Zhang, Di Liu, Ting Liu, and Dimitris N Metaxas · 2023
Closest in time.
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
Closest in time.
Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning
James Seale Smith, Leonid Karlinsky, Vyshnavi Gutta, Paola Cascante-Bonilla, Donghyun Kim, Assaf Arbelle, Rameswar Panda, Rogerio Feris, and Zsolt Kira · 2023
Closest in time.
Incorporating neuro-inspired adaptability for continual learning in artificial intelligence
Liyuan Wang, Xingxing Zhang, Qian Li, Mingtian Zhang, Hang Su, Jun Zhu, and Yi Zhong · 2023
Closest in time.
A comprehensive survey of continual learning: Theory, method and application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu · 2023
Closest in time.
Slca: Slow learner with classifier alignment for continual learning on a pre-trained model
Gengwei Zhang, Liyuan Wang, Guoliang Kang, Ling Chen, and Yunchao Wei · 2023
Closest in time.