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| Bayesiansk metrikindlæring× | Few-shot Learning× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2010s | 2011–2017 |
| Ophavsperson≠ | Multiple (Xing et al. 2002; Weinberger & Saul 2009; probabilistic extensions by various authors ~2010s) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| Type≠ | Probabilistic distance metric learning | Meta-learning / low-data learning paradigm |
| Oprindelig kilde≠ | Weinberger, K. Q., & Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10, 207–244. 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 ↗ |
| Aliasser | BML, probabilistic metric learning, Bayesian distance metric learning, Bayesian similarity learning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| Relaterede≠ | 5 | 4 |
| Resumé≠ | Bayesian Metric Learning frames the problem of learning a task-adapted distance function as probabilistic inference. Rather than producing a single optimal metric matrix, it places a prior over metrics, updates it with pairwise similarity or label constraints, and yields a posterior distribution that quantifies uncertainty about which metric best captures the true structure of the data. | 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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