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	Aktywne uczenie wspomagane uczeniem samo nadzorowanym	Propagacja etykiet
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	2020–2021	2002
Twórca	Bengar et al. and concurrent works (multiple groups)	Zhu, X. & Ghahramani, Z.
Typ	Hybrid active-learning and self-supervised pre-training framework	Graph-based semi-supervised classification
Źródło pierwotne	Bengar, J. Z., van de Weijer, J., Twardowski, B., & Raducanu, B. (2021). Reducing Label Effort: Self-Supervised Meets Active Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 1631–1639.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Inne nazwy	SSL-AL, self-supervised active learning, semi-supervised active learning with self-supervision, label-efficient self-supervised learning	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Pokrewne	5	3
Podsumowanie	Self-supervised Active Learning (SSL-AL) is a label-efficient machine-learning paradigm that pre-trains a model on unlabeled data using self-supervised objectives, then strategically queries a human oracle for the most informative labels using an active-learning acquisition function. The result is strong predictive performance with a fraction of the annotation cost required by fully supervised approaches.	Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.