Сравнение методов

	Ансамблевое метрическое обучение	Перенос обучения
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2000s–2010s	2010 (formalized); 1990s (early roots)
Автор метода	Multiple contributors (Weinberger, Saul, et al.)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Тип	Ensemble of learned distance metrics	Learning paradigm
Основополагающий источник	Wang, J., Kalousis, A., & Woznica, A. (2012). Parametric local metric learning for nearest neighbor classification. Advances in Neural Information Processing Systems, 25.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Другие названия	EML, ensemble distance metric learning, multiple metric fusion, combined metric learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Связанные	5	3
Сводка	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.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.