Salīdzināt metodes

	Aktīvā mācīšanās ar pašuzraudzītu mācīšanos	Pašuzraudzības apmācība
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2020-2022	2018–2020
Autors	Multiple authors (active learning + SSL integration, 2020s)	LeCun, Y. and community (formalized ~2018–2020)
Tips	Hybrid learning paradigm	Representation 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.	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	AL-SSL, active self-supervised learning, self-supervised active learning, query-based self-supervised learning	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Saistītās	6	3
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.	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.