Machine learningMachine learning
自监督主动学习
自监督主动学习（SSL-AL）是一种标签效率高的机器学习范式，它首先使用自监督目标在无标签数据上预训练模型，然后使用主动学习的获取函数策略性地向人类专家查询信息量最大的标签。其结果是以远低于全监督方法所需的标注成本获得了强大的预测性能。
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来源
- Bengar, J. Z., van de Weijer, J., Twardowski, B., & Raducanu, B. (2021). Reducing Label Effort: Self-Supervised Meets Active Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 1631–1639. link ↗
- Zhan, X., Wang, Q., Huang, K.-H., Xiong, H., Dou, D., & Chan, A. B. (2022). A comparative survey of deep active learning. arXiv preprint arXiv:2203.13450. link ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Active Learning (SSL-AL hybrid label-efficient framework). ScholarGate. https://scholargate.app/zh/machine-learning/self-supervised-active-learning
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