Salīdzināt metodes

	Metriskā apguve	Pašuzraudzības apmācība
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2003 (foundational); refined 2009 (LMNN)	2018–2020
Autors	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.	LeCun, Y. and community (formalized ~2018–2020)
Tips	Representation learning / supervised distance optimization	Representation learning paradigm
Pirmavots	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.	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/
Citi nosaukumi	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Saistītās	5	3
Kopsavilkums	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.	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.