Võrdle meetodeid

	Meetriline õppimine	Enesest juhendav õppimine	Siiami närvivõrk
Valdkond	Masinõpe	Masinõpe	Süvaõpe
Perekond	Machine learning	Machine learning	Machine learning
Tekkeaasta	2003 (foundational); refined 2009 (LMNN)	2018–2020	1993
Looja	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.	LeCun, Y. and community (formalized ~2018–2020)	Jane Bromley & Yann LeCun et al.; popularized by Koch et al.
Tüüp	Representation learning / supervised distance optimization	Representation learning paradigm	Deep metric-learning architecture
Algallikas	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/	Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., & Shah, R. (1993). Signature verification using a 'Siamese' time delay neural network. Advances in Neural Information Processing Systems, 6.
Rööpnimetused	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning	twin network, Siamese neural network, contrastive metric network, Siyam ağı
Seotud	5	3	1
Kokkuvõte	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.	A Siamese network is a deep architecture with two (or more) identical, weight-sharing branches that map inputs into an embedding space where similar inputs land close together and dissimilar ones far apart. Introduced by Bromley, LeCun, and colleagues in 1993 for signature verification and revived by Koch et al. (2015) for one-shot image recognition, it learns a similarity metric rather than fixed class labels, making it ideal for verification, matching, and few-shot tasks.