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Incremental Learning (IL) aims to learn deep models on sequential tasks continually, where each new task includes a batch of new classes and deep models have no access to task-ID information at the inference time.
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Y. Wu, Y. Chen, L. Wang, Y. Ye, Z. Liu, Y. Guo, and Y. Fu, “Large scale incremental learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 374–382
2019
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2019
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R. Aljundi, K. Kelchtermans, and T. Tuytelaars, “Task-free continual learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 11 254–11 263
2019
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M. Masana, X. Liu, B. Twardowski, M. Menta, A. D. Bagdanov, and J. Van De Weijer, “Class-incremental learning: survey and performance evaluation on image classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 5, pp. 5513–5533, 2022
2022
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Z. Mai, R. Li, J. Jeong, D. Quispe, H. Kim, and S. Sanner, “Online continual learning in image classification: An empirical survey,” Neurocomputing , vol. 469, pp. 28–51, 2022
2022
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J. Dong, L. Wang, Z. Fang, G. Sun, S. Xu, X. Wang, and Q. Zhu, “Federated class-incremental learning,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022
2022
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2022
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X. Li, Y. Zhou, T. Wu, R. Socher, and C. Xiong, “Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting,” in International Conference on Machine Learning . PMLR, 2019, pp. 3925–3934
2019
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A. Barbu, D. Mayo, J. Alverio, W. Luo, C. Wang, D. Gutfreund, J. Tenenbaum, and B. Katz, “Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models,” Advances in neural information processing systems , vol. 32, 2019
2019
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2019
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P. Buzzega, M. Boschini, A. Porrello, D. Abati, and S. Calderara, “Dark experience for general continual learning: a strong, simple baseline,” Advances in neural information processing systems , vol. 33, pp. 15 920–15 930, 2020
2020
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2020
Cited alongside, same era.
2020
Cited alongside, same era.
Z. Ke, B. Liu, and X. Huang, “Continual learning of a mixed sequence of similar and dissimilar tasks,” Advances in Neural Information Processing Systems , vol. 33, pp. 18 493–18 504, 2020
2020
Cited alongside, same era.
H. Cha, J. Lee, and J. Shin, “Co2l: Contrastive continual learning,” in Proceedings of the IEEE/CVF International conference on computer vision , 2021, pp. 9516–9525
2021
Cited alongside, same era.
P. Mazumder, P. Singh, P. Rai, and V. P. Namboodiri, “Rectification-based knowledge retention for task incremental learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 3, pp. 1561–1575, 2022
2022
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Z. Ji, J. Li, Q. Wang, and Z. Zhang, “Complementary calibration: Boosting general continual learning with collaborative distillation and self-supervision,” IEEE transactions on image processing , vol. 32, pp. 657–667, 2022
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2022
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2022
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Y. Zhang, Z. Yin, J. Shao, and Z. Liu, “Benchmarking omni-vision representation through the lens of visual realms,” in European Conference on Computer Vision . Springer, 2022, pp. 594–611
2022
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2023
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G. Zhang, L. Wang, G. Kang, L. Chen, and Y. Wei, “Slca: Slow learner with classifier alignment for continual learning on a pre-trained model,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 19 148–19 158
2023
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J. S. Smith, L. Karlinsky, V. Gutta, P. Cascante-Bonilla, D. Kim, A. Arbelle, R. Panda, R. Feris, and Z. Kira, “Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 11 909–11 919
2023
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J. Dong, W. Liang, Y. Cong, and G. Sun, “Heterogeneous forgetting compensation for class-incremental learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2023, pp. 11 742–11 751
2023
Later among the works it cites.
X. Li, S. Wang, J. Sun, and Z. Xu, “Variational data-free knowledge distillation for continual learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 10, pp. 12 618–12 634, 2023
2023
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Q. Pham, C. Liu, and S. C. Hoi, “Continual learning, fast and slow,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
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Z. Ji, Z. Hou, X. Liu, Y. Pang, and X. Li, “Memorizing complementation network for few-shot class-incremental learning,” IEEE Transactions on Image Processing , vol. 32, pp. 937–948, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Dong, H. Li, Y. Cong, G. Sun, Y. Zhang, and L. Van Gool, “No one left behind: Real-world federated class-incremental learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 4, pp. 2054–2070, 2024
2024
Closest in time.
L. Wang, X. Zhang, H. Su, and J. Zhu, “A comprehensive survey of continual learning: theory, method and application,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
2024
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J. Lu and S. Sun, “Pamk: Prototype augmented multi-teacher knowledge transfer network for continual zero-shot learning,” IEEE Transactions on Image Processing , 2024
2024
Closest in time.
Y. Lv, Y. Yan, J.-H. Xue, S. Chen, and H. Wang, “Relationship-guided knowledge transfer for class-incremental facial expression recognition,” IEEE Transactions on Image Processing , vol. 33, pp. 2293–2304, 2024
2024
Closest in time.
W. Zhang, K. Ma, G. Zhai, and X. Yang, “Task-specific normalization for continual learning of blind image quality models,” IEEE Transactions on Image Processing , 2024
2024
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Q. Wang, Y. Wu, L. Yang, W. Zuo, and Q. Hu, “Layer-specific knowledge distillation for class incremental semantic segmentation,” IEEE Transactions on Image Processing , 2024
2024
Closest in time.
K. Wei, X. Yang, Z. Xu, and C. Deng, “Class-incremental unsupervised domain adaptation via pseudo-label distillation,” IEEE Transactions on Image Processing , 2024
2024
Closest in time.