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Continual learning (CL) aims to continually accumulate knowledge from a non-stationary data stream without catastrophic forgetting of learned knowledge, requiring a balance between stability and adaptability.
On tiny episodic memories in continual learning
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc’Aurelio Ranzato · 1902
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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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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Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein · 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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Expert gate: Lifelong learning with a network of experts
Rahaf Aljundi, Punarjay Chakravarty, and Tinne Tuytelaars · 2017
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Fearnet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 2017
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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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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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Variational continual learning
Cuong V Nguyen, Yingzhen Li, Thang D Bui, and Richard E Turner · 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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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Deep learning using rectified linear units (relu). arxiv 2018
Abien Fred Agarap · 2018
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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
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Memory replay gans: Learning to generate images from new categories without forgetting [c]
WU Chenshen, L Herranz, LIU Xialei, et al · 2018
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Lifelong learning via progressive distillation and retrospection
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2018
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Predicting the computational cost of deep learning models
Daniel Justus, John Brennan, Stephen Bonner, and Andrew Stephen McGough · 2018
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Piggyback: Adapting a single network to multiple tasks by learning to mask weights
Arun Mallya, Dillon Davis, and Svetlana Lazebnik · 2018
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Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 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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Task agnostic continual learning using online variational bayes
Chen Zeno, Itay Golan, Elad Hoffer, and Daniel Soudry · 2018
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Uncertainty-based continual learning with adaptive regularization
Hongjoon Ahn, Sungmin Cha, Donggyu Lee, and Taesup Moon · 2019
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Efficient lifelong learning with A-GEM
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2019
Cited alongside, same era.
Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection
Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and Anton van den Hengel · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Compacting, picking and growing for unforgetting continual learning
Ching-Yi Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen · 2019
Cited alongside, same era.
Overcoming catastrophic forgetting with unlabeled data in the wild
Kibok Lee, Kimin Lee, Jinwoo Shin, and Honglak Lee · 2019
Cited alongside, same era.
Visual prompt tuning
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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Convolutional bypasses are better vision transformer adapters
Shibo Jie and Zhi-Hong Deng · 2022
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Training-free uncertainty estimation for dense regression: Sensitivity as a surrogate
Lu Mi, Hao Wang, Yonglong Tian, Hao He, and Nir N Shavit · 2022
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Simpler is better: off-the-shelf continual learning through pretrained backbones
Francesco Pelosin · 2022
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AdaMix: Mixture-of-adaptations for parameter-efficient model tuning
Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, and Jianfeng Gao · 2022
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Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting
Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, and Caiming Xiong · 2019
Cited alongside, same era.
Toward understanding catastrophic forgetting in continual learning
Cuong V Nguyen, Alessandro Achille, Michael Lam, Tal Hassner, Vijay Mahadevan, and Stefano Soatto · 2019
Cited alongside, same era.
Scalable recollections for continual lifelong learning
Matthew Riemer, Tim Klinger, Djallel Bouneffouf, and Michele Franceschini · 2019
Cited alongside, same era.
Complementary learning for overcoming catastrophic forgetting using experience replay
Mohammad Rostami, Soheil Kolouri, and Praveen K Pilly · 2019
Cited alongside, same era.
A large-scale study of representation learning with the visual task adaptation benchmark
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, et al · 2019
Cited alongside, same era.
Dark experience for general continual learning: a strong, simple baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara · 2020
Cited alongside, same era.
Embracing change: Continual learning in deep neural networks
Raia Hadsell, Dushyant Rao, Andrei A Rusu, and Razvan Pascanu · 2020
Cited alongside, same era.
Learning bayesian sparse networks with full experience replay for continual learning
Qingsen Yan, Dong Gong, Yuhang Liu, Anton van den Hengel, and Javen Qinfeng Shi · 2022
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Task-free continual learning via online discrepancy distance learning
Fei Ye and Adrian G Bors · 2022
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Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
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Lifelong language pretraining with distribution-specialized experts
Wuyang Chen, Yanqi Zhou, Nan Du, Yanping Huang, James Laudon, Zhifeng Chen, and Claire Cui · 2023
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Adaptersoup: Weight averaging to improve generalization of pretrained language models
Alexandra Chronopoulou, Matthew E. Peters, Alexander Fraser, and Jesse Dodge · 2023
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Generalized few-shot continual learning with contrastive mixture of adapters
Yawen Cui, Zitong Yu, Rizhao Cai, Xun Wang, Alex C Kot, and Li Liu · 2023
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Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Jun Zhao, Wei Shen, Yuhao Zhou, Zhiheng Xi, Xiao Wang, Xiaoran Fan, et al · 2023
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A unified continual learning framework with general parameter-efficient tuning
Qiankun Gao, Chen Zhao, Yifan Sun, Teng Xi, Gang Zhang, Bernard Ghanem, and Jian Zhang · 2023
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Mixture of cluster-conditional lora experts for vision-language instruction tuning
Yunhao Gou, Zhili Liu, Kai Chen, Lanqing Hong, Hang Xu, Aoxue Li, Dit-Yan Yeung, James T Kwok, and Yu Zhang · 2023
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Surgical fine-tuning improves adaptation to distribution shifts
Yoonho Lee, Annie S. Chen, Fahim Tajwar, Ananya Kumar, Huaxiu Yao, Percy Liang, and Chelsea Finn · 2023
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RanPAC: Random projections and pre-trained models for continual learning
Mark McDonnell, Dong Gong, Amin Parvaneh, Ehsan Abbasnejad, and Anton van den Hengel · 2023
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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
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Pilot: A pre-trained model-based continual learning toolbox
Hai-Long Sun, Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2023
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Self-evolved dynamic expansion model for task-free continual learning
Fei Ye and Adrian G Bors · 2023
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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
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Preventing zero-shot transfer degradation in continual learning of vision-language models
Zangwei Zheng, Mingyuan Ma, Kai Wang, Ziheng Qin, Xiangyu Yue, and Yang You · 2023
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Higher layers need more lora experts, 2024
Chongyang Gao, Kezhen Chen, Jinmeng Rao, Baochen Sun, Ruibo Liu, Daiyi Peng, Yawen Zhang, Xiaoyuan Guo, Jie Yang, and VS Subrahmanian · 2024
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CLAP4CLIP: Continual learning with probabilistic finetuning for vision-language models
Saurav Jha, Dong Gong, and Lina Yao · 2024
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Inflora: Interference-free low-rank adaptation for continual learning
Yan-Shuo Liang and Wu-Jun Li · 2024
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Convolutional prompting meets language models for continual learning
Anurag Roy, Riddhiman Moulick, Vinay K Verma, Saptarshi Ghosh, and Abir Das · 2024
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A comprehensive survey of continual learning: Theory, method and application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu · 2024
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MoLE: Mixture of loRA experts
Xun Wu, Shaohan Huang, and Furu Wei · 2024
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Expandable subspace ensemble for pre-trained model-based class-incremental learning
Da-Wei Zhou, Hai-Long Sun, Han-Jia Ye, and De-Chuan Zhan · 2024
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