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| 온라인 능동 학습× | 퓨샷 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2000s | 2011–2017 |
| 창시자≠ | Cesa-Bianchi, N. and others (multiple contributors) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| 유형≠ | Hybrid learning paradigm (online + active) | Meta-learning / low-data learning paradigm |
| 원전≠ | Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2006). Worst-case analysis of selective sampling for linear classification. Journal of Machine Learning Research, 7, 1205–1230. 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 | streaming active learning, online query-by-committee, sequential active learning, incremental active learning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| 관련≠ | 6 | 4 |
| 요약≠ | Online active learning combines two complementary paradigms: it processes data as a stream (online learning) and selectively requests labels only for the most informative instances (active learning). The result is a model that adapts continuously to new data while keeping labeling costs low — useful whenever labeled data is expensive and examples arrive sequentially rather than all at once. | 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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