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| 준지도 소수샷 학습× | 퓨샷 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2018 | 2011–2017 |
| 창시자≠ | Ren, M. et al. (ICLR 2018); builds on Finn et al. (MAML, 2017) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| 유형≠ | Meta-learning with unlabeled auxiliary data | Meta-learning / low-data learning paradigm |
| 원전≠ | 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 ↗ | 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 ↗ |
| 별칭 | SS-FSL, semi-supervised meta-learning, few-shot learning with unlabeled data, low-label few-shot learning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| 관련 | 4 | 4 |
| 요약≠ | 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. | 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. |
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