Machine learningMachine learning
半监督少样本学习
半监督少样本学习（SS-FSL）旨在训练模型，使其能够仅凭每类少量标记样本对新类别进行分类，同时利用大量无标记数据来丰富类别表示。通过将元学习的训练过程与对无标记样本的软伪标签分配相结合，当存在大量无标记数据时，SS-FSL可以实现比纯监督少样本方法显著更高的准确率。
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来源
- Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., & Zemel, R. S. (2018). Meta-learning for semi-supervised few-shot classification. In International Conference on Learning Representations (ICLR 2018). link ↗
- Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning (ICML 2017), PMLR 70, 1126–1135. link ↗
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Few-shot Learning (SS-FSL). ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-few-shot-learning
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