Salīdzināt metodes

	Pašuzraudzības metriskā apguve	Pašuzraudzības apmācība
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2020 (modern contrastive formulation); foundations 1990s–2000s	2018–2020
Autors	Chen, T. et al. (SimCLR); earlier metric learning foundations by Bromley, LeCun (1994)	LeCun, Y. and community (formalized ~2018–2020)
Tips	Self-supervised representation learning with metric objective	Representation learning paradigm
Pirmavots	Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119, 1597–1607.	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	self-supervised representation learning with metric loss, contrastive self-supervised learning, unsupervised metric learning, SSML	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Saistītās	3	3
Kopsavilkums	Self-supervised metric learning trains a neural encoder to embed inputs so that semantically similar items lie close together in vector space, using automatically generated pseudo-labels instead of human annotations. By combining self-supervised pretext tasks with contrastive or triplet-based metric objectives, it produces transferable, label-efficient representations applicable to retrieval, clustering, and few-shot classification.	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.