Salīdzināt metodes

	Pašuzraudzītā aktīvā mācīšanās	Iezīmju izplatīšana
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2020–2021	2002
Autors	Bengar et al. and concurrent works (multiple groups)	Zhu, X. & Ghahramani, Z.
Tips	Hybrid active-learning and self-supervised pre-training framework	Graph-based semi-supervised classification
Pirmavots	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.
Citi nosaukumi	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
Saistītās	5	3
Kopsavilkums	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.