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With the advent of large-scale pre-trained models, interest in adapting and exploiting them for continual learning scenarios has grown.
Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, M.; and Cohen, N. J. 1989 · 1989
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Brain mechanisms linking language and action
Pulvermüller, F. 2005 · 2005
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J.; Pascanu, R.; Rabinowitz, N.; Veness, J.; Desjardins, G.; Rusu, A. A.; Milan, K.; Quan, J.; Ramalho, T.; Grabska-Barwinska, A.; et al. 2017 · 2017
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icarl: Incremental classifier and representation learning
Rebuffi, S.-A.; Kolesnikov, A.; Sperl, G.; and Lampert, C. H. 2017 · 2017
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Continual Learning with Deep Generative Replay
Shin, H.; Lee, J. K.; Kim, J.; and Kim, J. 2017 · 2017
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Continual learning through synaptic intelligence
Zenke, F.; Poole, B.; and Ganguli, S. 2017 · 2017
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Memory aware synapses: Learning what (not) to forget
Aljundi, R.; Babiloni, F.; Elhoseiny, M.; Rohrbach, M.; and Tuytelaars, T. 2018 · 2018
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End-to-end incremental learning
Castro, F. M.; Marín-Jiménez, M. J.; Guil, N.; Schmid, C.; and Alahari, K. 2018 · 2018
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Rotate your networks: Better weight consolidation and less catastrophic forgetting
Liu, X.; Masana, M.; Herranz, L.; Van de Weijer, J.; Lopez, A. M.; and Bagdanov, A. D. 2018 · 2018
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Piggyback: Adapting a single network to multiple tasks by learning to mask weights
Mallya, A.; Davis, D.; and Lazebnik, S. 2018 · 2018
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Packnet: Adding multiple tasks to a single network by iterative pruning
Mallya, A.; and Lazebnik, S. 2018 · 2018
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Progress & Compress: A scalable framework for continual learning
Schwarz, J.; Czarnecki, W.; Luketina, J.; Grabska-Barwinska, A.; Whye Teh, Y.; Pascanu, R.; and Hadsell, R. 2018 · 2018
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Overcoming Catastrophic Forgetting with Hard Attention to the Task
Serra, J.; Suris, D.; Miron, M.; and Karatzoglou, A. 2018 · 2018
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Il2m: Class incremental learning with dual memory
Belouadah, E.; and Popescu, A. 2019 · 2019
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Learning a Unified Classifier Incrementally via Rebalancing
Hou, S.; Pan, X.; Loy, C. C.; Wang, Z.; and Lin, D. 2019 · 2019
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Continual lifelong learning with neural networks: A review
Parisi, G. I.; Kemker, R.; Part, J. L.; Kanan, C.; and Wermter, S. 2019 · 2019
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Random path selection for incremental learning
Rajasegaran, J.; Hayat, M.; Khan, S.; Khan, F. S.; and Shao, L. 2019 · 2019
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Three scenarios for continual learning
Van de Ven, G. M.; and Tolias, A. S. 2019 · 2019
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Large Scale Incremental Learning
Wu, Y.; Chen, Y.; Wang, L.; Ye, Y.; Liu, Z.; Guo, Y.; and Fu, Y. 2019 · 2019
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A comprehensive study of class incremental learning algorithms for visual tasks
Belouadah, E.; Popescu, A.; and Kanellos, I. 2020 · 2020
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Uniter: Universal image-text representation learning
Chen, Y.-C.; Li, L.; Yu, L.; El Kholy, A.; Ahmed, F.; Gan, Z.; Cheng, Y.; and Liu, J. 2020 · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2020 · 2020
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Podnet: Pooled outputs distillation for small-tasks incremental learning
Douillard, A.; Cord, M.; Ollion, C.; Robert, T.; and Valle, E. 2020 · 2020
Align before fuse: Vision and language representation learning with momentum distillation
Li, J.; Selvaraju, R.; Gotmare, A.; Joty, S.; Xiong, C.; and Hoi, S. C. H. 2021 · 2021
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Adaptive aggregation networks for class-incremental learning
Liu, Y.; Schiele, B.; and Sun, Q. 2021 · 2021
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Ternary feature masks: continual learning without any forgetting
Masana, M.; Tuytelaars, T.; and van de Weijer, J. 2021 · 2021
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Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
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On learning the geodesic path for incremental learning
Simon, C.; Koniusz, P.; and Harandi, M. 2021 · 2021
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Rehearsal revealed: The limits and merits of revisiting samples in continual learning
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Cited alongside, same era.
Remind your neural network to prevent catastrophic forgetting
Hayes, T. L.; Kafle, K.; Shrestha, R.; Acharya, M.; and Kanan, C. 2020 · 2020
Cited alongside, same era.
Oscar: Object-semantics aligned pre-training for vision-language tasks
Li, X.; Yin, X.; Li, C.; Zhang, P.; Hu, X.; Zhang, L.; Wang, L.; Hu, H.; Dong, L.; Wei, F.; et al. 2020 · 2020
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Gdumb: A simple approach that questions our progress in continual learning
Prabhu, A.; Torr, P. H.; and Dokania, P. K. 2020 · 2020
Cited alongside, same era.
Topology-preserving class-incremental learning
Tao, X.; Chang, X.; Hong, X.; Wei, X.; and Gong, Y. 2020 · 2020
Cited alongside, same era.
Semantic drift compensation for class-incremental learning
Yu, L.; Twardowski, B.; Liu, X.; Herranz, L.; Wang, K.; Cheng, Y.; Jui, S.; and Weijer, J. v. d. 2020 · 2020
Cited alongside, same era.
Maintaining discrimination and fairness in class incremental learning
Zhao, B.; Xiao, X.; Gan, G.; Zhang, B.; and Xia, S.-T. 2020 · 2020
Cited alongside, same era.
Verwimp, E.; De Lange, M.; and Tuytelaars, T. 2021 · 2021
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DER: Dynamically Expandable Representation for Class Incremental Learning
Yan, S.; Xie, J.; and He, X. 2021 · 2021
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Continual pre-training mitigates forgetting in language and vision
Cossu, A.; Tuytelaars, T.; Carta, A.; Passaro, L.; Lomonaco, V.; and Bacciu, D. 2022 · 2022
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Memory efficient continual learning with transformers
Ermis, B.; Zappella, G.; Wistuba, M.; and Archambeau, C. 2022 · 2022
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Class-incremental learning: survey and performance evaluation
Masana, M.; Liu, X.; Twardowski, B.; Menta, M.; Bagdanov, A. D.; and van de Weijer, J. 2022 · 2022
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Rusu, A. A.; Rabinowitz, N. C.; Desjardins, G.; Soyer, H.; Kirkpatrick, J.; Kavukcuoglu, K.; Pascanu, R.; and Hadsell, R. 2022 · 2022
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CLIP model is an Efficient Continual Learner
Thengane, V.; Khan, S.; Hayat, M.; and Khan, F. 2022 · 2022
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Class-incremental learning with strong pre-trained models
Wu, T.-Y.; Swaminathan, G.; Li, Z.; Ravichandran, A.; Vasconcelos, N.; Bhotika, R.; and Soatto, S. 2022 · 2022
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Meta-attention for ViT-backed Continual Learning
Xue, M.; Zhang, H.; Song, J.; and Song, M. 2022 · 2022
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POP: Prompt Of Prompts for Continual Learning
Hu, Z.; Lyu, J.; Gao, D.; and Vasconcelos, N. 2023 · 2023
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A Simple Baseline that Questions the Use of Pretrained-Models in Continual Learning
Janson, P.; Zhang, W.; Aljundi, R.; and Elhoseiny, M. 2023 · 2023
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Progressive Prompts: Continual Learning for Language Models
Razdaibiedina, A.; Mao, Y.; Hou, R.; Khabsa, M.; Lewis, M.; and Almahairi, A. 2023 · 2023
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