方法对比
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| 少样本学习× | 度量学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2011–2017 | 2003 (foundational); refined 2009 (LMNN) |
| 提出者≠ | Lake, B. M.; Vinyals, O.; Finn, C. et al. | Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y. |
| 类型≠ | Meta-learning / low-data learning paradigm | Representation learning / supervised distance optimization |
| 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 ↗ | Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2003). Distance metric learning with application to clustering with side-information. In Advances in Neural Information Processing Systems (NIPS), 16, 505–512. link ↗ |
| 别名 | FSL, low-shot learning, k-shot learning, meta-learning for few examples | Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance |
| 相关≠ | 4 | 5 |
| 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. | Metric learning is a machine-learning framework that trains a distance or similarity function from data so that semantically similar examples end up close together in the learned space while dissimilar examples are pushed apart. Unlike fixed distances such as Euclidean, the learned metric adapts to the structure of the task, making downstream classifiers, clusterers, and retrieval systems significantly more accurate. |
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