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Continual learning requires to overcome catastrophic forgetting when training a single model on a sequence of tasks.
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The caltech-ucsd birds-200-2011 dataset
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Conceptual representations in mind and brain: Theoretical developments, current evidence and future directions
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Imagenet large scale visual recognition challenge
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Overcoming catastrophic forgetting in neural networks
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Overcoming catastrophic forgetting by incremental moment matching
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Learning without forgetting
Z. Li and D. Hoiem · 2017
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Gradient episodic memory for continual learning
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Neural discrete representation learning
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Attention is all you need
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Memory aware synapses: Learning what (not) to forget
R. Aljundi, F. Babiloni, M. Elhoseiny, M. Rohrbach, and T. Tuytelaars · 2018
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Selective experience replay for lifelong learning
D. Isele and A. Cosgun · 2018
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2018
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Overcoming catastrophic forgetting with hard attention to the task
J. Serra, D. Suris, M. Miron, and A. Karatzoglou · 2018
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Lifelong learning with dynamically expandable networks
J. Yoon, E. Yang, J. Lee, and S. J. Hwang · 2018
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Il2m: Class incremental learning with dual memory
E. Belouadah and A. Popescu · 2019
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On tiny episodic memories in continual learning
A. a. Chaudhry, M. Rohrbach, M. Elhoseiny, T. Ajanthan, P. K. Dokania, P. H. Torr, and M. Ranzato · 2019
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Learning a unified classifier incrementally via rebalancing
S. Hou, X. Pan, C. C. Loy, Z. Wang, and D. Lin · 2019
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Compacting, picking and growing for unforgetting continual learning
C.-Y. Hung, C.-H. Tu, C.-E. Wu, C.-H. Chen, Y.-M. Chan, and C.-S. Chen · 2019
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Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting
X. Li, Y. Zhou, T. Wu, R. Socher, and C. Xiong · 2019
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Continual lifelong learning with neural networks: A review
Prefix-tuning: Optimizing continuous prompts for generation
X. L. Li and P. Liang · 2021
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Imagenet-21k pretraining for the masses
T. Ridnik, E. Ben-Baruch, A. Noy, and L. Zelnik-Manor · 2021
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Gradient projection memory for continual learning
G. Saha, I. Garg, and K. Roy · 2021
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Dytox: Transformers for continual learning with dynamic token expansion
A. Douillard, A. Ramé, G. Couairon, and M. Cord · 2022
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Balancing stability and plasticity through advanced null space in continual learning
Y. Kong, L. Liu, Z. Wang, and D. Tao · 2022
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Residual tuning: Toward novel category discovery without labels
Y. Liu and T. Tuytelaars · 2022
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G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter · 2019
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Learning to learn without forgetting by maximizing transfer and minimizing interference
M. Riemer, I. Cases, R. Ajemian, M. Liu, I. Rish, Y. Tu, and G. Tesauro · 2019
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Experience replay for continual learning
D. Rolnick, A. Ahuja, J. Schwarz, T. Lillicrap, and G. Wayne · 2019
Cited alongside, same era.
Three scenarios for continual learning
G. M. Van de Ven and A. S. Tolias · 2019
Cited alongside, same era.
Large scale incremental learning
Y. Wu, Y. Chen, L. Wang, Y. Ye, Z. Liu, Y. Guo, and Y. Fu · 2019
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Continual learning of context-dependent processing in neural networks
G. Zeng, Y. Chen, B. Cui, and S. Yu · 2019
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A large-scale study of representation learning with the visual task adaptation benchmark
X. Zhai, J. Puigcerver, A. Kolesnikov, P. Ruyssen, C. Riquelme, M. Lucic, J. Djolonga, A. S. Pinto, M. Neumann, A. Dosovitskiy, et al · 2019
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Class-incremental learning: Survey and performance evaluation on image classification
M. Masana, X. Liu, B. Twardowski, M. Menta, A. D. Bagdanov, and J. van de Weijer · 2022
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Mimicking the oracle: an initial phase decorrelation approach for class incremental learning
Y. Shi, K. Zhou, J. Liang, Z. Jiang, J. Feng, P. H. Torr, S. Bai, and V. Y. Tan · 2022
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Image bert pre-training with online tokenizer
J. Zhou, C. Wei, H. Wang, W. Shen, C. Xie, A. Yuille, and T. Kong · 2022
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Rebalancing batch normalization for exemplar-based class-incremental learning
S. Cha, S. Cho, D. Hwang, S. Hong, M. Lee, and T. Moon · 2023
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Parameter-level soft-masking for continual learning
T. Konishi, M. Kurokawa, C. Ono, Z. Ke, G. Kim, and B. Liu · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig · 2023
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Class-incremental exemplar compression for class-incremental learning
Z. Luo, Y. Liu, B. Schiele, and Q. Sun · 2023
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Napa-vq: Neighborhood-aware prototype augmentation with vector quantization for continual learning
T. Malepathirana, D. Senanayake, and S. Halgamuge · 2023
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Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning
J. S. Smith, L. Karlinsky, V. Gutta, P. Cascante-Bonilla, D. Kim, A. Arbelle, R. Panda, R. Feris, and Z. Kira · 2023
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When prompt-based incremental learning does not meet strong pretraining
Y.-M. Tang, Y.-X. Peng, and W.-S. Zheng · 2023
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Slca: Slow learner with classifier alignment for continual learning on a pre-trained model
G. Zhang, L. Wang, G. Kang, L. Chen, and Y. Wei · 2023
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Evolving parameterized prompt memory for continual learning
M. R. Kurniawan, X. Song, Z. Ma, Y. He, Y. Gong, Y. Qi, and X. Wei · 2024
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