Comparar métodos

	Aprendizaje activo auto-supervisado	Propagación de Etiquetas
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2020–2021	2002
Autor original	Bengar et al. and concurrent works (multiple groups)	Zhu, X. & Ghahramani, Z.
Tipo	Hybrid active-learning and self-supervised pre-training framework	Graph-based semi-supervised classification
Fuente seminal	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.
Alias	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
Relacionados	5	3
Resumen	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.