Sammenlign metoder

	Regulariseret semisuperviseret læring	Selvovervåget læring
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2006	2018–2020
Ophavsperson	Belkin, M.; Niyogi, P.; Sindhwani, V.	LeCun, Y. and community (formalized ~2018–2020)
Type	Regularized learning paradigm	Representation learning paradigm
Oprindelig kilde	Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434.	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/
Aliasser	manifold regularization, graph-regularized SSL, semi-supervised regularization, Laplacian regularization	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Relaterede	6	3
Resumé	Regularized semi-supervised learning adds explicit geometric or graph-based penalty terms to a semi-supervised objective so that the decision function varies smoothly over the data manifold. Pioneered through manifold regularization (Belkin, Niyogi & Sindhwani, 2006), it exploits the structure of both labeled and unlabeled examples to learn more accurate models than supervised regularization alone when labeled data are scarce.	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.