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。
| 少样本学习× | 半监督学习× | 迁移学习× | |
|---|---|---|---|
| 领域 | 机器学习 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning | Machine learning |
| 起源年份≠ | 2011–2017 | 1970s–2006 (formalized) | 2010 (formalized); 1990s (early roots) |
| 提出者≠ | Lake, B. M.; Vinyals, O.; Finn, C. et al. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| 类型≠ | Meta-learning / low-data learning paradigm | Learning paradigm | Learning paradigm |
| 开创性文献≠ | Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| 别名 | FSL, low-shot learning, k-shot learning, meta-learning for few examples | SSL, semi-supervised machine learning, transductive learning, label-efficient learning | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| 相关≠ | 4 | 5 | 3 |
| 摘要≠ | Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited. | 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. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
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