Comparer des méthodes

	Apprentissage semi-supervisé en ensemble	Apprentissage auto-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1998–2005	2018–2020
Auteur d'origine	Blum & Mitchell (co-training); Zhou & Li (tri-training)	LeCun, Y. and community (formalized ~2018–2020)
Type	Ensemble + semi-supervised hybrid paradigm	Representation learning paradigm
Source fondatrice	Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529–1541.	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/
Alias	semi-supervised ensemble, SSL ensemble, ensemble-based SSL, co-training ensemble	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Apparentées	6	3
Résumé	Ensemble semi-supervised learning combines multiple base learners with the semi-supervised paradigm, exploiting both a small labeled set and a large pool of unlabeled data. By letting diverse classifiers teach each other through pseudo-labeling or co-training, the ensemble improves generalization far beyond what either approach alone could achieve with limited labels.	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.