방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 퓨샷 학습× | 메트릭 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2011–2017 | 2003 (foundational); refined 2009 (LMNN) |
| 창시자≠ | Lake, B. M.; Vinyals, O.; Finn, C. et al. | Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y. |
| 유형≠ | Meta-learning / low-data learning paradigm | Representation learning / supervised distance optimization |
| 원전≠ | 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 ↗ |
| 별칭 | FSL, low-shot learning, k-shot learning, meta-learning for few examples | Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance |
| 관련≠ | 4 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|