Salīdzināt metodes

	Aktīvā mācīšanās ar pašuzraudzītu mācīšanos	Daudzpusīgā apguve
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2020-2022	1970s–2006 (formalized)
Autors	Multiple authors (active learning + SSL integration, 2020s)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tips	Hybrid learning paradigm	Learning paradigm
Pirmavots	Bengar, J. Z., van de Weijer, J., Fuentes, L. L., & Raducanu, B. (2022). Class-Balanced Active Learning for Image Classification. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 3082–3091.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Citi nosaukumi	AL-SSL, active self-supervised learning, self-supervised active learning, query-based self-supervised learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Saistītās	6	5
Kopsavilkums	Active learning combined with self-supervised learning leverages unlabeled data through self-supervised pre-training to build rich representations, then uses an active query strategy to select the most informative examples for human annotation, maximizing model performance under a tight labeling budget. This hybrid approach is especially powerful when labeled data is scarce but large unlabeled pools exist.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.