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Continual learning (CL) is a learning paradigm that emulates the human capability of learning and accumulating knowledge continually without forgetting the previously learned knowledge and also transferring the learned knowledge to help learn new tasks better.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Transforming auto-encoders
Geoffrey E Hinton, Alex Krizhevsky, and Sida D Wang · 2011
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Mind the gap: assessing the conformance of software traceability to relevant guidelines
Patrick Rempel, Patrick Mäder, Tobias Kuschke, and Jane Cleland-Huang · 2014
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Expert gate: Lifelong learning with a network of experts
Rahaf Aljundi, Punarjay Chakravarty, and Tinne Tuytelaars · 2016
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2016
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Progressive neural networks
Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Pathnet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A. Rusu, Alexander Pritzel, and Daan Wierstra · 2017
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Toward continual learning for conversational agents
Sungjin Lee · 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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Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton · 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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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Lifelong machine learning
Zhiyuan Chen and Bing Liu · 2018
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Overcoming catastrophic interference using conceptor-aided backpropagation
Xu He and Herbert Jaeger · 2018
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FearNet: Brain-Inspired Model for Incremental Learning
Ronald Kemker and Christopher Kanan · 2018
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Packnet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
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The natural language decathlon: Multitask learning as question answering
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 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 Serrà, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2018
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Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 2019
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Efficient lifelong learning with A-GEM
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2019
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Episodic memory in lifelong language learning
Cyprien de Masson d’Autume, Sebastian Ruder, Lingpeng Kong, and Dani Yogatama · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 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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Compacting, picking and growing for unforgetting continual learning
Steven C. Y. Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen · 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, and Tinne Tuytelaars · 2019
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Continual learning for sentence representations using conceptors
Tianlin Liu, Lyle Ungar, and João Sedoc · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
Cited alongside, same era.
Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2019
Cited alongside, same era.
A progressive model to enable continual learning for semantic slot filling
Yilin Shen, Xiangyu Zeng, and Hongxia Jin · 2019
Cited alongside, same era.
Three scenarios for continual learning
Gido M. van de Ven and Andreas S. Tolias · 2019
Cited alongside, same era.
Continuous learning of context-dependent processing in neural networks
Guanxiong Zeng, Yang Chen, Bo Cui, and Shan Yu · 2019
Cited alongside, same era.
Classic: Continual and contrastive learning of aspect sentiment classification tasks
Zixuan Ke, Bing Liu, Hu Xu, and Lei Shu · 2021
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Adapting bert for continual learning of a sequence of aspect sentiment classification tasks
Zixuan Ke, Hu Xu, and Bing Liu · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Lifelong intent detection via multi-strategy rebalancing
Qingbin Liu, Xiaoyan Yu, Shizhu He, Kang Liu, and Jun Zhao · 2021
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Time waits for no one! analysis and challenges of temporal misalignment
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Magdalena Biesialska, Katarzyna Biesialska, and Marta R Costa-jussà · 2020
Cited alongside, same era.
Uncertainty-guided continual learning with bayesian neural networks
Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, and Marcus Rohrbach · 2020
Cited alongside, same era.
The turking test: Can language models understand instructions?
Avia Efrat and Omer Levy · 2020
Cited alongside, same era.
La-maml: Look-ahead meta learning for continual learning
Gunshi Gupta, Karmesh Yadav, and Liam Paull · 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.
Continual learning of a mixed sequence of similar and dissimilar tasks
Zixuan Ke, Bing Liu, and Xingchang Huang · 2020
Cited alongside, same era.
Continual learning with knowledge transfer for sentiment classification
Zixuan Ke, Bing Liu, Hao Wang, and Lei Shu · 2020
Cited alongside, same era.
Kelvin Luu, Daniel Khashabi, Suchin Gururangan, Karishma Mandyam, and Noah A Smith · 2021
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Continual learning for named entity recognition
Natawut Monaikul, Giuseppe Castellucci, Simone Filice, and Oleg Rokhlenko · 2021
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Recent advances of continual learning in computer vision: An overview
Haoxuan Qu, Hossein Rahmani, Li Xu, Bryan Williams, and Jun Liu · 2021
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Paul Röttger and Janet B Pierrehumbert · 2021
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Mell: Large-scale extensible user intent classification for dialogue systems with meta lifelong learning
Chengyu Wang, Haojie Pan, Yuan Liu, Kehan Chen, Minghui Qiu, Wei Zhou, Jun Huang, Haiqing Chen, Wei Lin, and Deng Cai · 2021
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Training networks in null space of feature covariance for continual learning
Shipeng Wang, Xiaorong Li, Jian Sun, and Zongben Xu · 2021
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Incremental few-shot text classification with multi-round new classes: Formulation, dataset and system
Congying Xia, Wenpeng Yin, Yihao Feng, and Philip S. Yu · 2021
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Time-aware language models as temporal knowledge bases
Bhuwan Dhingra, Jeremy R Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, and William W Cohen · 2022
Closest in time.
Continual training of language models for few-shot learning
Zixuan Ke, Haowei Lin, Yijia Shao, Hu Xu, Lei Shu, and Bing Liu · 2022
Closest in time.
A theoretical study on solving continual learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, Zixuan Ke, and Bing Liu · 2022
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Overcoming catastrophic forgetting during domain adaptation of seq2seq language generation
Dingcheng Li, Zheng Chen, Eunah Cho, Jie Hao, Xiaohu Liu, Fan Xing, Chenlei Guo, and Yang Liu · 2022
Closest in time.
Continual few-shot intent detection
Guodun Li, Yuchen Zhai, Qianglong Chen, Xing Gao, Ji Zhang, and Yin Zhang · 2022
Closest in time.
Beyond not-forgetting: Continual learning with backward knowledge transfer
Sen Lin, Li Yang, Deliang Fan, and Junshan Zhang · 2022
Closest in time.
TRGP: Trust region gradient projection for continual learning
Sen Lin, Li Yang, Deliang Fan, and Junshan Zhang · 2022
Closest in time.
Timelms: Diachronic language models from twitter
Daniel Loureiro, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, and Jose Camacho-Collados · 2022
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Online continual learning in image classification: An empirical survey
Zheda Mai, Ruiwen Li, Jihwan Jeong, David Quispe, Hyunwoo Kim, and Scott Sanner · 2022
Closest in time.
Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi · 2022
Closest in time.
LFPT5: A unified framework for lifelong few-shot language learning based on prompt tuning of T5
Chengwei Qin and Shafiq R. Joty · 2022
Closest in time.
Elle: Efficient lifelong pre-training for emerging data
Yujia Qin, Jiajie Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou · 2022
Closest in time.
Continual-t0: Progressively instructing 50+ tasks to language models without forgetting
Thomas Scialom, Tuhin Chakrabarty, and Smaranda Muresan · 2022
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Prompt augmented generative replay via supervised contrastive learning for lifelong intent detection
Vaibhav Varshney, Mayur Patidar, Rajat Kumar, Lovekesh Vig, and Gautam Shroff · 2022
Closest in time.
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
Closest in time.
Contintin: Continual learning from task instructions
Wenpeng Yin, Jia Li, and Caiming Xiong · 2022
Closest in time.
Continual sequence generation with adaptive compositional modules
Yanzhe Zhang, Xuezhi Wang, and Diyi Yang · 2022
Closest in time.
Prompt conditioned VAE: enhancing generative replay for lifelong learning in task-oriented dialogue
Yingxiu Zhao, Yinhe Zheng, Zhiliang Tian, Chang Gao, Bowen Yu, Haiyang Yu, Yongbin Li, Jian Sun, and Nevin L. Zhang · 2022
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Continual prompt tuning for dialog state tracking
Qi Zhu, Bing Li, Fei Mi, Xiaoyan Zhu, and Minlie Huang · 2022
Closest in time.