Salīdzināt metodes

	Tiešsaistes daļēji uzraudzītā apguve	Pašuzraudzības apmācība
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2000s–2010s	2018–2020
Autors	Goldberg, A., Li, M., & Zhu, X. (and others in stream learning community)	LeCun, Y. and community (formalized ~2018–2020)
Tips	Incremental / stream-based semi-supervised learning framework	Representation learning paradigm
Pirmavots	Goldberg, A., Li, M., & Zhu, X. (2008). Online manifold regularization: A new learning setting and empirical study. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 393–407. Springer.	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	stream-based semi-supervised learning, incremental semi-supervised learning, online SSL, semi-supervised online learning	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Saistītās	6	3
Kopsavilkums	Online semi-supervised learning combines the incremental, one-pass nature of online learning with the ability to exploit unlabeled data alongside sparse labeled observations. It is designed for settings where data arrives as a stream and obtaining labels for every instance is expensive or impractical — such as real-time classification of web content, sensor readings, or social media posts.	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.