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| 강건 거리 학습× | 퓨샷 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2009–2012 | 2011–2017 |
| 창시자≠ | Various (Weinberger, Saul, Schultz et al.; robust extensions by Shen, Cao and others, 2009–2012) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| 유형≠ | Supervised/semi-supervised distance metric learning with robustness to noise and outliers | Meta-learning / low-data learning paradigm |
| 원전≠ | Shen, C., Kim, J., Wang, L., & van den Hengel, A. (2012). Positive Semidefinite Metric Learning Using Boosting-like Algorithms. Journal of Machine Learning Research, 13, 1007–1036. 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 | robust distance metric learning, noise-robust metric learning, outlier-robust similarity learning, robust DML | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| 관련≠ | 5 | 4 |
| 요약≠ | Robust Metric Learning learns a Mahalanobis distance function from labeled or pairwise-constrained data while actively resisting the distortion caused by noisy labels, corrupted examples, or outliers. By replacing standard hinge or squared losses with robust alternatives and adding regularization, it produces a distance metric that generalises well even when the training set is imperfect — a common situation in real-world scientific and applied tasks. | 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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