مقایسهٔ روش‌ها

	یادگیری متریک ترکیبی (Ensemble Metric Learning)	یادگیری چند نمونه‌ای (Few-shot Learning)
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	2000s–2010s	2011–2017
پدیدآور	Multiple contributors (Weinberger, Saul, et al.)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
نوع	Ensemble of learned distance metrics	Meta-learning / low-data learning paradigm
منبع بنیادین	Wang, J., Kalousis, A., & Woznica, A. (2012). Parametric local metric learning for nearest neighbor classification. Advances in Neural Information Processing Systems, 25.	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.
نام‌های دیگر	EML, ensemble distance metric learning, multiple metric fusion, combined metric learning	FSL, low-shot learning, k-shot learning, meta-learning for few examples
مرتبط	5	4
خلاصه	Ensemble Metric Learning trains multiple distance metric learners — each on a different data view, feature subspace, or with a different objective — and combines the resulting metrics to produce a single, more robust similarity function. Combining diverse metrics reduces the variance of any individual metric and improves performance in tasks such as nearest-neighbor classification, retrieval, and few-shot learning.	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.