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Catastrophic forgetting is a significant challenge in online continual learning (OCL), especially for non-stationary data streams that do not have well-defined task boundaries.
A practical bayesian framework for backpropagation networks
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A practical bayesian framework for backpropagation networks
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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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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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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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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Core50: a new dataset and benchmark for continuous object recognition
Vincenzo Lomonaco and Davide Maltoni · 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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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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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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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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Core50: a new dataset and benchmark for continuous object recognition
Vincenzo Lomonaco and Davide Maltoni · 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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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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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
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End-to-end incremental learning
Francisco Manuel Castro, Manuel J. Marín-Jiménez, Nicolás Guil Mata, Cordelia Schmid, and Alahari Karteek · 2018
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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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Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2018
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Towards robust evaluations of continual learning
Sebastian Farquhar and Yarin Gal · 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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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
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End-to-end incremental learning
Francisco Manuel Castro, Manuel J. Marín-Jiménez, Nicolás Guil Mata, Cordelia Schmid, and Alahari Karteek · 2018
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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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Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2018
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Towards robust evaluations of continual learning
Sebastian Farquhar and Yarin Gal · 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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Online continual learning with maximal interfered retrieval
Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, and Lucas Page-Caccia · 2019
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Task-free continual learning
Rahaf Aljundi, Klaas Kelchtermans, and Tinne Tuytelaars · 2019
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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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Continual learning with tiny episodic memories
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, P Dokania, P Torr, and M Ranzato · 2019
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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 · 2019
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Task agnostic continual learning via meta learning
Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A Rusu, Yee Whye Teh, and Razvan Pascanu · 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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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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Random path selection for continual learning
Jathushan Rajasegaran, Munawar Hayat, Salman H Khan, Fahad Shahbaz Khan, and Ling Shao · 2019
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Continual unsupervised representation learning
Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh, and Raia Hadsell · 2019
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Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 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
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Online continual learning with maximal interfered retrieval
Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, and Lucas Page-Caccia · 2019
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Task-free continual learning
Rahaf Aljundi, Klaas Kelchtermans, and Tinne Tuytelaars · 2019
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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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Continual learning with tiny episodic memories
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, P Dokania, P Torr, and M Ranzato · 2019
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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 · 2019
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Task agnostic continual learning via meta learning
Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A Rusu, Yee Whye Teh, and Razvan Pascanu · 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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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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Random path selection for continual learning
Jathushan Rajasegaran, Munawar Hayat, Salman H Khan, Fahad Shahbaz Khan, and Ling Shao · 2019
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Continual unsupervised representation learning
Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh, and Raia Hadsell · 2019
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Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 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
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Dark experience for general continual learning: a strong, simple baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara · 2020
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Online continual learning from imbalanced data
Aristotelis Chrysakis and Marie-Francine Moens · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Adversarial continual learning
Sayna Ebrahimi, Franziska Meier, Roberto Calandra, Trevor Darrell, and Marcus Rohrbach · 2020
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Remind your neural network to prevent catastrophic forgetting
Tyler L Hayes, Kushal Kafle, Robik Shrestha, Manoj Acharya, and Christopher Kanan · 2020
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Gradient based memory editing for task-free continual learning
Xisen Jin, Junyi Du, and Xiang Ren · 2020
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Continual learning of a mixed sequence of similar and dissimilar tasks
Zixuan Ke, Bing Liu, and Xingchang Huang · 2020
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Imbalanced continual learning with partitioning reservoir sampling
Chris Dongjoo Kim, Jinseo Jeong, and Gunhee Kim · 2020
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Clinical applications of continual learning machine learning
Cecilia S Lee and Aaron Y Lee · 2020
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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
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Meta-attention for vit-backed continual learning
Mengqi Xue, Haofei Zhang, Jie Song, and Mingli Song · 2022
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A simple but strong baseline for online continual learning: Repeated augmented rehearsal
Yaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert Bifet, Nick Jin Sean Lim, and Yunzhe Jia · 2022
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Adaptformer: Adapting vision transformers for scalable visual recognition
Shoufa Chen, Chongjian Ge, Zhan Tong, Jiangliu Wang, Yibing Song, Jue Wang, and Ping Luo · 2022
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Not just selection, but exploration: Online class-incremental continual learning via dual view consistency
Yanan Gu, Xu Yang, Kun Wei, and Cheng Deng · 2022
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A neural dirichlet process mixture model for task-free continual learning
Soochan Lee, Junsoo Ha, Dongsu Zhang, and Gunhee Kim · 2020
Cited alongside, same era.
More classifiers, less forgetting: A generic multi-classifier paradigm for incremental learning
Yu Liu, Sarah Parisot, Gregory Slabaugh, Xu Jia, Ales Leonardis, and Tinne Tuytelaars · 2020
Cited alongside, same era.
Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip Torr, and Puneet Dokania · 2020
Cited alongside, same era.
Learn-prune-share for lifelong learning
Zifeng Wang, Tong Jian, Kaushik Chowdhury, Yanzhi Wang, Jennifer Dy, and Stratis Ioannidis · 2020
Cited alongside, same era.
Incremental learning via rate reduction
Ziyang Wu, Christina Baek, Chong You, and Yi Ma · 2020
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.
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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Helpful or harmful: Inter-task association in continual learning
Hyundong Jin and Eunwoo Kim · 2022
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Forget-free continual learning with winning subnetworks
Haeyong Kang, Rusty John Lloyd Mina, Sultan Rizky Hikmawan Madjid, Jaehong Yoon, Mark Hasegawa-Johnson, Sung Ju Hwang, and Chang D Yoo · 2022
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Scaling & shifting your features: A new baseline for efficient model tuning
Dongze Lian, Daquan Zhou, Jiashi Feng, and Xinchao Wang · 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
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Online task-free continual learning with dynamic sparse distributed memory
Julien Pourcel, Ngoc-Son Vu, and Robert M French · 2022
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Foster: Feature boosting and compression for class-incremental learning
Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 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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Online continual learning with contrastive vision transformer
Zhen Wang, Liu Liu, Yajing Kong, Jiaxian Guo, and Dacheng Tao · 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, Guolong Su, Vincent Perot, Jennifer Dy, et al · 2022
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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.
Meta-attention for vit-backed continual learning
Mengqi Xue, Haofei Zhang, Jie Song, and Mingli Song · 2022
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A simple but strong baseline for online continual learning: Repeated augmented rehearsal
Yaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert Bifet, Nick Jin Sean Lim, and Yunzhe Jia · 2022
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Streaming lifelong learning with any-time inference
Soumya Banerjee, Vinay Kumar Verma, and Vinay P Namboodiri · 2023
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Task arithmetic with lora for continual learning
Rajas Chitale, Ankit Vaidya, Aditya Kane, and Archana Ghotkar · 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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Loss decoupling for task-agnostic continual learning
Yan-Shuo Liang and Wu-Jun Li · 2023
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Pcr: Proxy-based contrastive replay for online class-incremental continual learning
Huiwei Lin, Baoquan Zhang, Shanshan Feng, Xutao Li, and Yunming Ye · 2023
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Online class incremental learning on stochastic blurry task boundary via mask and visual prompt tuning
Jun-Yeong Moon, Keon-Hee Park, Jung Uk Kim, and Gyeong-Moon Park · 2023
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Online class incremental learning on stochastic blurry task boundary via mask and visual prompt tuning
Jun-Yeong Moon, Keon-Hee Park, Jung Uk Kim, and Gyeong-Moon Park · 2023
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Progressive prompts: Continual learning for language models
Anastasia Razdaibiedina, Yuning Mao, Rui Hou, Madian Khabsa, Mike Lewis, and Amjad Almahairi · 2023
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Continual diffusion: Continual customization of text-to-image diffusion with c-lora
James Smith, Yen-Chang Hsu, Lingyu Zhang, Ting Hua, Zsolt Kira, Yilin Shen, and Hongxia Jin · 2023
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Construct-vl: Data-free continual structured vl concepts learning
James Seale Smith, Paola Cascante-Bonilla, Assaf Arbelle, Donghyun Kim, Rameswar Panda, David Cox, Diyi Yang, Zsolt Kira, Rogerio Feris, and Leonid Karlinsky · 2023
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Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning
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Improving online continual learning performance and stability with temporal ensembles
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Continual learning: Applications and the road forward
Eli Verwimp, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H Lampert, et al · 2023
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Continual learning with low rank adaptation
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Bayesian low-rank adaptation for large language models
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Slca: Slow learner with classifier alignment for continual learning on a pre-trained model
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Deep class-incremental learning: A survey
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Melo: Low-rank adaptation is better than fine-tuning for medical image diagnosis, 2023
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Streaming lifelong learning with any-time inference
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Task arithmetic with lora for continual learning
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Later among the works it cites.
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
Later among the works it cites.
Loss decoupling for task-agnostic continual learning
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Later among the works it cites.
Pcr: Proxy-based contrastive replay for online class-incremental continual learning
Huiwei Lin, Baoquan Zhang, Shanshan Feng, Xutao Li, and Yunming Ye · 2023
Later among the works it cites.
Online class incremental learning on stochastic blurry task boundary via mask and visual prompt tuning
Jun-Yeong Moon, Keon-Hee Park, Jung Uk Kim, and Gyeong-Moon Park · 2023
Later among the works it cites.
Online class incremental learning on stochastic blurry task boundary via mask and visual prompt tuning
Jun-Yeong Moon, Keon-Hee Park, Jung Uk Kim, and Gyeong-Moon Park · 2023
Later among the works it cites.
Progressive prompts: Continual learning for language models
Anastasia Razdaibiedina, Yuning Mao, Rui Hou, Madian Khabsa, Mike Lewis, and Amjad Almahairi · 2023
Later among the works it cites.
Continual diffusion: Continual customization of text-to-image diffusion with c-lora
James Smith, Yen-Chang Hsu, Lingyu Zhang, Ting Hua, Zsolt Kira, Yilin Shen, and Hongxia Jin · 2023
Later among the works it cites.
Construct-vl: Data-free continual structured vl concepts learning
James Seale Smith, Paola Cascante-Bonilla, Assaf Arbelle, Donghyun Kim, Rameswar Panda, David Cox, Diyi Yang, Zsolt Kira, Rogerio Feris, and Leonid Karlinsky · 2023
Later among the works it cites.
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
Later among the works it cites.
Improving online continual learning performance and stability with temporal ensembles
Albin Soutif-Cormerais, Antonio Carta, and Joost Van de Weijer · 2023
Later among the works it cites.
Continual learning: Applications and the road forward
Eli Verwimp, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H Lampert, et al · 2023
Later among the works it cites.
Continual learning with low rank adaptation
Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, and Giovanni Zappella · 2023
Later among the works it cites.
Bayesian low-rank adaptation for large language models
Adam X. Yang, Maxime Robeyns, Xi Wang, and Laurence Aitchison · 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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Deep class-incremental learning: A survey
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Melo: Low-rank adaptation is better than fine-tuning for medical image diagnosis, 2023
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A comprehensive survey of continual learning: theory, method and application
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Online task-free continual generative and discriminative learning via dynamic cluster memory
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Verse: Virtual-gradient aware streaming lifelong learning with anytime inference
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Dyson: Dynamic feature space self-organization for online task-free class incremental learning
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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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Deepspeed: A deep learning optimization library
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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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Online task-free continual generative and discriminative learning via dynamic cluster memory
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