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主动学习与自监督学习

主动学习结合自监督学习，通过自监督预训练利用未标记数据来构建丰富的表征，然后使用主动查询策略选择信息量最大的样本进行人工标注，从而在严格的标注预算下最大限度地提高模型性能。当标记数据稀缺但存在大量未标记数据池时，这种混合方法尤其强大。

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Method map

The neighbourhood of related methods — select a node to explore.

主动学习与自监督学习
主动学习少样本学习在线学习自监督学习半监督学习迁移学习

来源

  1. Bengar, J. Z., van de Weijer, J., Fuentes, L. L., & Raducanu, B. (2022). Class-Balanced Active Learning for Image Classification. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 3082–3091. link ↗
  2. Wang, K., Zhang, D., Li, Y., Zhang, R., & Lin, L. (2016). Cost-Effective Active Learning for Deep Image Classification. IEEE Transactions on Circuits and Systems for Video Technology, 27(12), 2591–2600. DOI: 10.1109/TCSVT.2016.2589879 ↗

如何引用本页

ScholarGate. (2026, June 3). Active Learning with Self-supervised Representation Learning. ScholarGate. https://scholargate.app/zh/machine-learning/active-learning-self-supervised-learning

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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ScholarGate — Active Learning Self-supervised Learning (Active Learning with Self-supervised Representation Learning). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/active-learning-self-supervised-learning · 数据集： https://doi.org/10.5281/zenodo.20539026