Page 105 - 《软件学报》2026年第4期
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1546 软件学报 2026 年第 37 卷第 4 期
[8] Fei GL, Wang S, Liu B. Learning cumulatively to become more knowledgeable. In: Proc. of the 22nd ACM SIGKDD Int’l Conf. on
Knowledge Discovery and Data Mining. San Francisco: ACM Press, 2016. 1565–1574. [doi: 10.1145/2939672.2939835]
[9] Li YJ, Yang X, Wang H, Wang XK, Li TR. Learning to prompt knowledge transfer for open-world continual learning. In: Proc. of the
38th AAAI Conf. on Artificial Intelligence. Vancouver: Association for the Advancement of Artificial Intelligence, 2024. 13700–13708.
[doi: 10.1609/aaai.v38i12.29275]
[10] Kim G, Xiao CN, Konishi T, Ke ZX, Liu B. Open-world continual learning: Unifying novelty detection and continual learning. Artificial
Intelligence, 2025, 338: 104237. [doi: 10.1016/j.artint.2024.104237]
[11] Dang SH, Cao ZJ, Cui ZY, Pi YM, Liu NY. Open set incremental learning for automatic target recognition. IEEE Trans. on Geoscience
and Remote Sensing, 2019, 57(7): 4445–4456. [doi: 10.1109/TGRS.2019.2891266]
[12] Kim G, Xiao CN, Konishi T, Ke ZX, Liu B. A theoretical study on solving continual learning. In: Proc. of the 36th Int’l Conf. on Neural
Information Processing Systems. New Orleans: Curran Associates Inc., 2022. 366.
[13] De Lange M, Aljundi R, Masana M, Parisot S, Jia X, Leonardis A, Slabaugh G, Tuytelaars T. A continual learning survey: Defying
forgetting in classification tasks. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2022, 44(7): 3366–3385. [doi: 10.1109/
TPAMI.2021.3057446]
[14] Kar S, Castellucci G, Filice S, Malmasi S, Rokhlenko O. Preventing catastrophic forgetting in continual learning of new natural language
tasks. In: Proc. of the 28th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining. Washington: ACM Press, 2022. 3137–3145.
[doi: 10.1145/3534678.3539169]
[15] Gao Q, Luo ZP, Klabjan D, Zhang FL. Efficient architecture search for continual learning. IEEE Trans. on Neural Networks and Learning
Systems, 2023, 34(11): 85555–8565. [doi: 10.1109/TNNLS.2022.3151511]
[16] Mallya A, Lazebnik S. PackNet: Adding multiple tasks to a single network by iterative pruning. In: Proc. of the 2018 IEEE/CVF Conf. on
Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018. 7765–7773. [doi: 10.1109/CVPR.2018.00810]
[17] Qin Q, Peng H, Hu WP, Zhao DY, Liu B. BNS: Building network structures dynamically for continual learning. In: Proc. of the 35th Int’l
Conf. on Neural Information Processing Systems. La Jolla: Curran Associates Inc., 2021. 1576.
[18] Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, Milan K, Quan J, Ramalho T, Grabska-Barwinska A,
Hassabis D, Clopath C, Kumaran D, Hadsell R. Overcoming catastrophic forgetting in neural networks. Proc. of the National Academy of
Sciences of the United States of America, 2017, 114(13): 3521–3526. [doi: 10.1073/pnas.1611835114]
[19] Wu Y, Chen YP, Wang LJ, Ye YC, Liu ZC, Guo YD, Fu Y. Large scale incremental learning. In: Proc. of the 2019 IEEE/CVF Conf. on
Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019. 374–382. [doi: 10.1109/CVPR.2019.00046]
[20] Wang LY, Lei B, Li Q, Su H, Zhu J, Zhong Y. Triple-memory networks: A brain-inspired method for continual learning. IEEE Trans. on
Neural Networks and Learning Systems, 2022, 33(5): 1925–1934. [doi: 10.1109/TNNLS.2021.3111019]
[21] Wu YQ, Xie RB, Zhu YC, Zhuang FZ, Zhang X, Lin LY, He Qing. Personalized prompt for sequential recommendation. IEEE Trans. on
Knowledge and Data Engineering, 2024 36(7): 3376–3389.
[22] Chaudhry A, Ranzato MA, Rohrbach M, Elhoseiny M. Efficient lifelong learning with A-GEM. In: Proc. of the 7th Int’l Conf. on
Learning Representations. New Orleans: OpenReview.net, 2019. 1–20.
[23] Li ZZ, Hoiem D. Learning without forgetting. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2018, 40(12): 2935–2947. [doi:
10.1109/TPAMI.2017.2773081]
[24] Yang R, Wang S, Zhang H, Xu SY, Guo YH, Ye XT, Hou B, Jiao LC. Knowledge decomposition and replay: A novel cross-modal image-
text retrieval continual learning method. In: Proc. of the 31st ACM Int’l Conf. on Multimedia. Ottawa: ACM Press, 2023. 6510–6519.
[doi: 10.1145/3581783.3612207]
[25] Lao QC, Jiang X, Havaei M, Bengio Y. A two-stream continual learning system with variational domain-agnostic feature replay. IEEE
Trans. on Neural Networks and Learning Systems, 2022, 33(9): 4466–4478. [doi: 10.1109/TNNLS.2021.3057453]
[26] Ke ZX, Liu B, Ma NZ, Xu H, Shu L. Achieving forgetting prevention and knowledge transfer in continual learning. In: Proc. of the 35th
Int’l Conf. on Neural Information Processing Systems. La Jolla: Curran Associates Inc., 2021. 1719.
[27] Rebuffi SA, Kolesnikov A, Sperl G, Lampert CH. iCaRL: Incremental classifier and representation learning. In: Proc. of the 2017
IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017. 5533–5542. [doi: 10.1109/CVPR.2017.587]
[28] Wang ZF, Zhang ZZ, Lee CY, Zhang H, Sun RX, Ren XQ, Su GL, Perot V, Dy J, Pfister T. Learning to prompt for continual learning. In:
Proc. of the 2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 139–149. [doi: 10.1109/
CVPR52688.2022.00024]
[29] Le M, Nguyen A, Nguyen H, Nguyen T, Pham T, Van Ngo L, Ho N. Mixture of experts meets prompt-based continual learning. In: Proc.
of the 38th Int’l Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2024. 3781.

