Сравнение на методи

	Метрично обучение	Трансферно обучение
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2003 (foundational); refined 2009 (LMNN)	2010 (formalized); 1990s (early roots)
Създател	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Тип	Representation learning / supervised distance optimization	Learning paradigm
Основополагащ източник	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.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Други названия	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Свързани	5	3
Резюме	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.	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.