[1] H. Ahn, S. Cha, D. Lee, and T. Moon. Uncertainty-based continual learning with adaptive regularization. Advances in neural information processing systems, 32, 2019.
[2] R. Aljundi, F. Babiloni, M. Elhoseiny, M. Rohrbach, and T. Tuytelaars. Memory aware synapses: Learning what (not) to forget. In Proceedings of the European conference on computer vision (ECCV), pages 139-154, 2018.
[3] M. Anthony, P. L. Bartlett, P. L. Bartlett, et al. Neural network learning: Theoretical foundations, volume 9. Cambridge university press Cambridge, 1999.
[4] E. Arani, F. Sarfraz, and B. Zonooz. Learning fast, learning slow: A general continual learning method based on complementary learning system. arXiv preprint arXiv:2201.12604, 2022.
[5] S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan. A theory of learning from different domains. Machine learning, 79:151-175, 2010.
[6] C. M. Bishop. Pattern recognition and machine learning. Springer, 2006.
[7] M. Boschini, L. Bonicelli, P. Buzzega, A. Porrello, and S. Calderara. Class-incremental continual learning into the extended der-verse. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
[8] 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, 33:15920-15930, 2020.
[9] H. Cha, J. Lee, and J. Shin. Co2l: Contrastive continual learning. In Proceedings of the IEEE/CVF International conference on computer vision, pages 9516-9525, 2021.
[10] A. Chaudhry, M. Ranzato, M. Rohrbach, and M. Elhoseiny. Efficient lifelong learning with a-gem. arXiv preprint arXiv:1812.00420, 2018.
[11] A. Chaudhry, M. Rohrbach, M. Elhoseiny, T. Ajanthan, P. K. Dokania, P. H. Torr, and M. Ranzato. On tiny episodic memories in continual learning. arXiv preprint arXiv:1902.10486, 2019.
[12] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020.
[13] T. Chen, H. Shi, S. Tang, Z. Chen, F. Wu, and Y. Zhuang. Cil: Contrastive instance learning framework for distantly supervised relation extraction. arXiv preprint arXiv:2106.10855, 2021.
[14] X. Chen, H. Fan, R. Girshick, and K. He. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297, 2020.
[15] Z. Chen and B. Liu. Lifelong machine learning, volume 1. Springer, 2018.
[16] Z. Chen, J. Zhuang, X. Liang, and L. Lin. Blending-target domain adaptation by adversarial meta-adaptation networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2248-2257, 2019.
[17] S. Dai, K. Sohn, Y.-H. Tsai, L. Carin, and M. Chandraker. Adaptation across extreme variations using unlabeled domain bridges. arXiv preprint arXiv:1906.02238, 2019.
[18] Y. Dai, H. Lang, Y. Zheng, B. Yu, F. Huang, and Y. Li. Domain incremental lifelong learning in an open world. arXiv preprint arXiv:2305.06555, 2023.
[19] M. Davari, N. Asadi, S. Mudur, R. Aljundi, and E. Belilovsky. Probing representation forgetting in supervised and unsupervised continual learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16712-16721, 2022.