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Recent work using pretrained transformers has shown impressive performance when fine-tuned with data from the downstream problem of interest.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Catastrophic forgetting in connectionist networks
Robert M French · 1999
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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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CORe50: a new dataset and benchmark for continuous object recognition
Vincenzo Lomonaco and Davide Maltoni · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Fearnet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 2018
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Online structured laplace approximations for overcoming catastrophic forgetting
Hippolyt Ritter, Aleksandar Botev, and David Barber · 2018
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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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Learning without memorizing
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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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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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Incremental learning for the detection and classification of gan-generated images
Francesco Marra, Cristiano Saltori, Giulia Boato, and Luisa Verdoliva · 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
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Pytorch image models
Ross Wightman · 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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Latent replay for real-time continual learning
Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, and Davide Maltoni · 2020
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Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2021
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Dytox: Transformers for continual learning with dynamic token expansion
Arthur Douillard, Alexandre Ramé, Guillaume Couairon, and Matthieu Cord · 2022
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Memory efficient continual learning with transformers
Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, and Cédric Archambeau · 2022
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LoRA: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
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Effect of scale on catastrophic forgetting in neural networks
Vinay Venkatesh Ramasesh, Aitor Lewkowycz, and Ethan Dyer · 2022
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GDumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip HS Torr, and Puneet K Dokania · 2020
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Learning latent representations across multiple data domains using lifelong VAEGAN
Fei Ye and Adrian G. Bors · 2020
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Co2l: Contrastive continual learning
Hyuntak Cha, Jaeho Lee, and Jinwoo Shin · 2021
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A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, and Tinne Tuytelaars · 2021
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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, Jakob Uszkoreit, and Neil Houlsby · 2021
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Sebastian Ruder, Jonas Pfeiffer, and Ivan Vulić · 2022
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On transferability of prompt tuning for natural language processing
Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, and Jie Zhou · 2022
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S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning
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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
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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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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, Rogério Feris, and Zsolt Kira · 2023
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