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Continual learning is a learning paradigm that learns tasks sequentially with resources constraints, in which the key challenge is stability-plasticity dilemma, i.e., it is uneasy to simultaneously have the stability to prevent catastrophic forgetting of old tasks and the plasticity to learn new tasks well.
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Chen, H.J., Cheng, A.C., Juan, D.C., Wei, W., Sun, M.: Mitigating forgetting in online continual learning via instance-aware parameterization. Advances in Neural Information Processing Systems 33
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Jerfel, G., Grant, E., Griffiths, T., Heller, K.A.: Reconciling meta-learning and continual learning with online mixtures of tasks. In: Advances in Neural Information Processing Systems. pp. 9122–9133 (2019)
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Tang, S., Chen, D., Zhu, J., Yu, S., Ouyang, W.: Layerwise optimization by gradient decomposition for continual learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9634–9643 (2021)
2021
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Wang, S., Li, X., Sun, J., Xu, Z.: Training networks in null space of feature covariance for continual learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 184–193 (June 2021)
2021
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Lin, S., Yang, L., Fan, D., Zhang, J.: Trgp: Trust region gradient projection for continual learning (2022)
2022
Closest in time.
Wang, Z., Liu, L., Duan, Y., Kong, Y., Tao, D.: Continual learning with lifelong vision transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 171–181 (June 2022)
2022
Closest in time.
Wang, Z., Liu, L., Duan, Y., Tao, D.: Continual learning through retrieval and imagination. Proceedings of the AAAI Conference on Artificial Intelligence 36
2022
Closest in time.
Yiduo, G., Wenpeng, H., Dongyan, Z., Bing, L.: Adaptive orthogonal projection for continual learning. AAAI (2022)
2022
Closest in time.