Krahasoni metodat

	Mësimi i Metrikës	Mësimi i Vetë-Mbikëqyrur
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	2003 (foundational); refined 2009 (LMNN)	2018–2020
Krijuesi	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.	LeCun, Y. and community (formalized ~2018–2020)
Lloji	Representation learning / supervised distance optimization	Representation learning paradigm
Burimi themelues	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/
Emërtime të tjera	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Të lidhura	5	3
Përmbledhja	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.