方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 半监督少样本学习× | 半监督学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2018 | 1970s–2006 (formalized) |
| 提出者≠ | Ren, M. et al. (ICLR 2018); builds on Finn et al. (MAML, 2017) | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| 类型≠ | Meta-learning with unlabeled auxiliary data | Learning paradigm |
| 开创性文献≠ | 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 ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| 别名 | SS-FSL, semi-supervised meta-learning, few-shot learning with unlabeled data, low-label few-shot learning | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| 相关≠ | 4 | 5 |
| 摘要≠ | Semi-supervised Few-shot Learning (SS-FSL) trains models to classify new classes from only a handful of labeled examples per class, while simultaneously leveraging a pool of unlabeled data to enrich class representations. By combining meta-learning episodes with soft pseudo-label assignment for unlabeled samples, it achieves notably higher accuracy than purely supervised few-shot methods when abundant unlabeled data is available. | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
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