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Continual learning methods based on pre-trained models (PTM) have recently gained attention which adapt to successive downstream tasks without catastrophic forgetting.
A large-scale study of representation learning with the visual task adaptation benchmark
Zhai, X.; Puigcerver, J.; Kolesnikov, A.; Ruyssen, P.; Riquelme, C.; Lucic, M.; Djolonga, J.; Pinto, A. S.; Neumann, M.; Dosovitskiy, A.; et al. 2019 · 1910
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Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, M.; and Cohen, N. J. 1989 · 1989
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Feed forward neural networks with random weights
Schmidt, W. F.; Kraaijveld, M. A.; Duin, R. P.; et al. 1992 · 1992
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A rapid supervised learning neural network for function interpolation and approximation
Chen, C. P. 1996 · 1996
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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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 · 2010
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icarl: Incremental classifier and representation learning
Rebuffi, S.-A.; Kolesnikov, A.; Sperl, G.; and Lampert, C. H. 2017a · 2010
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The caltech-ucsd birds-200-2011 dataset
Wah, C.; Branson, S.; Welinder, P.; Perona, P.; and Belongie, S. 2011 · 2011
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On the importance of pair-wise feature correlations for image classification
McDonnell, M. D.; McKilliam, R. G.; and de Chazal, P. 2016 · 2016
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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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Gradient episodic memory for continual learning
Lopez-Paz, D.; and Ranzato, M. 2017 · 2017
Cited alongside, same era.
Lifelong machine learning
Chen, Z.; and Liu, B. 2018 · 2018
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Frankle, J.; and Carbin, M. 2018 · 2018
Cited alongside, same era.
Learning without Forgetting
Li, Z.; and Hoiem, D. 2018 · 2018
Cited alongside, same era.
Lifelong Learning with Dynamically Expandable Networks
Yoon, J.; Yang, E.; Lee, J.; and Hwang, S. J. 2018 · 2018
Cited alongside, same era.
A continual learning survey: Defying forgetting in classification tasks
De Lange, M.; Aljundi, R.; Masana, M.; Parisot, S.; Jia, X.; Leonardis, A.; Slabaugh, G.; and Tuytelaars, T. 2021 · 2021
Cited alongside, same era.
Simpler is better: off-the-shelf continual learning through pretrained backbones
Pelosin, F. 2022 · 2022
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A model or 603 exemplars: Towards memory-efficient class-incremental learning
Zhou, D.-W.; Wang, Q.-W.; Ye, H.-J.; and Zhan, D.-C. 2022 · 2022
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A unified continual learning framework with general parameter-efficient tuning
Gao, Q.; Zhao, C.; Sun, Y.; Xi, T.; Zhang, G.; Ghanem, B.; and Zhang, J. 2023 · 2023
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Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning
Smith, J. S.; Karlinsky, L.; Gutta, V.; Cascante-Bonilla, P.; Kim, D.; Arbelle, A.; Panda, R.; Feris, R.; and Kira, Z. 2023 · 2023
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L3DOC: Lifelong 3D Object Classification
Liu, Y.; Cong, Y.; Sun, G.; Zhang, T.; Dong, J.; and Liu, H. 2021 · 2021
Cited alongside, same era.
Der: Dynamically expandable representation for class incremental learning
Yan, S.; Xie, J.; and He, X. 2021 · 2021
Cited alongside, same era.
A Continual Learning Survey: Defying Forgetting in Classification Tasks
De Lange, M.; Aljundi, R.; Masana, M.; Parisot, S.; Jia, X.; Leonardis, A., Leonardis; Slabaugh, G.; and Tuytelaars, T. 2022 · 2022
Cited alongside, same era.
A simple baseline that questions the use of pretrained-models in continual learning
Janson, P.; Zhang, W.; Aljundi, R.; and Elhoseiny, M. 2022 · 2022
Cited alongside, same era.
Online continual learning in image classification: An empirical survey
Mai, Z.; Li, R.; Jeong, J.; Quispe, D.; Kim, H.; and Sanner, S. 2022 · 2022
Cited alongside, same era.
The many faces of robustness: A critical analysis of out-of-distribution generalization
Hendrycks, D.; Basart, S.; Mu, N.; Kadavath, S.; Wang, F.; Dorundo, E.; Desai, R.; Zhu, T.; Parajuli, S.; Guo, M.; et al. 2021a
Cited in the paper.
Sun, H.-L.; Zhou, D.-W.; Ye, H.-J.; and Zhan, D.-C. 2023 · 2023
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Isolation and impartial aggregation: A paradigm of incremental learning without interference
Wang, Y.; Ma, Z.; Huang, Z.; Wang, Y.; Su, Z.; and Hong, X. 2023 · 2023
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Language models are super mario: Absorbing abilities from homologous models as a free lunch
Yu, L.; Yu, B.; Yu, H.; Huang, F.; and Li, Y. 2023 · 2023
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Slca: Slow learner with classifier alignment for continual learning on a pre-trained model
Zhang, G.; Wang, L.; Kang, G.; Chen, L.; and Wei, Y. 2023 · 2023
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Ranpac: Random projections and pre-trained models for continual learning
McDonnell, M. D.; Gong, D.; Parvaneh, A.; Abbasnejad, E.; and van den Hengel, A. 2024 · 2024
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Hierarchical decomposition of prompt-based continual learning: Rethinking obscured sub-optimality
Wang, L.; Xie, J.; Zhang, X.; Huang, M.; Su, H.; and Zhu, J. 2024 · 2024
Closest in time.
Continual Learning with Pre-Trained Models: A Survey
Zhou, D.-W.; Sun, H.-L.; Ning, J.; Ye, H.-J.; and Zhan, D.-C. 2024 · 2024
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