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Class-incremental learning (CIL) aims to continuously introduce novel categories into a classification system without forgetting previously learned ones, thus adapting to evolving data distributions.
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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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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icarl: Incremental classifier and representation learning
Rebuffi, S.-A.; Kolesnikov, A.; Sperl, G.; and Lampert, C. H. 2017b · 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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Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
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Less-forgetting learning in deep neural networks
Jung, H.; Ju, J.; Jung, M.; and Kim, J. 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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Learning without forgetting
Li, Z.; and Hoiem, D. 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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Memory aware synapses: Learning what (not) to forget
Aljundi, R.; Babiloni, F.; Elhoseiny, M.; Rohrbach, M.; and Tuytelaars, T. 2018 · 2018
Cited alongside, same era.
Riemannian walk for incremental learning: Understanding forgetting and intransigence
Chaudhry, A.; Dokania, P. K.; Ajanthan, T.; and Torr, P. H. 2018 · 2018
Cited alongside, same era.
Learning to remember: A synaptic plasticity driven framework for continual learning
Ostapenko, O.; Puscas, M.; Klein, T.; Jahnichen, P.; and Nabi, M. 2019 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. 2019 · 2019
Cited alongside, same era.
Large scale incremental learning
Wu, Y.; Chen, Y.; Wang, L.; Ye, Y.; Liu, Z.; Guo, Y.; and Fu, Y. 2019 · 2019
Cited alongside, same era.
Incremental learning using conditional adversarial networks
S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning
Wang, Y.; Huang, Z.; and Hong, X. 2022 · 2022
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Benchmarking omni-vision representation through the lens of visual realms
Zhang, Y.; Yin, Z.; Shao, J.; and Liu, Z. 2022 · 2022
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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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PILOT: A Pre-Trained Model-Based Continual Learning Toolbox
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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Xiang, Y.; Fu, Y.; Ji, P.; and Huang, H. 2019 · 2019
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Lester, B.; Al-Rfou, R.; and Constant, N. 2021 · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L.; and Liang, P. 2021 · 2021
Cited alongside, same era.
Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Zaken, E. B.; Ravfogel, S.; and Goldberg, Y. 2021 · 2021
Cited alongside, same era.
Visual prompt tuning
Jia, M.; Tang, L.; Chen, B.-C.; Cardie, C.; Belongie, S.; Hariharan, B.; and Lim, S.-N. 2022 · 2022
Cited alongside, same era.
Class-incremental learning: survey and performance evaluation on image classification
Masana, M.; Liu, X.; Twardowski, B.; Menta, M.; Bagdanov, A. D.; and Van De Weijer, J. 2022 · 2022
Cited alongside, same era.
Zhou, D.-W.; Ye, H.-J.; Zhan, D.-C.; and Liu, Z. 2023 · 2023
Later among the works it cites.
Consistent Prompting for Rehearsal-Free Continual Learning
Gao, Z.; Cen, J.; and Chang, X. 2024 · 2024
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OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning
Huang, W.-C.; Chen, C.-F.; and Hsu, H. 2024 · 2024
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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
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Causal prompting: Debiasing large language model prompting based on front-door adjustment
Zhang, C.; Zhang, L.; Wu, J.; Zhou, D.; and He, Y. 2024 · 2024
Closest in time.
Open-World Dynamic Prompt and Continual Visual Representation Learning
Kim, Y.; Fang, J.; Zhang, Q.; Cai, Z.; Shen, Y.; Duggal, R.; S Raychaudhuri, D.; Tu, Z.; Xing, Y.; and Dabeer, O. 2025 · 2025
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