Comparer des méthodes

	Forêt aléatoire auto-supervisée	Propagation d'étiquettes
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2012–2022	2002
Auteur d'origine	Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage)	Zhu, X. & Ghahramani, Z.
Type	Semi-supervised ensemble (self-supervised pretext task + RF)	Graph-based semi-supervised classification
Source fondatrice	Lefortier, D., Chitta, K., & Agrawal, P. (2022). Self-supervised random forests. arXiv:2204.01430.	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-RF, self-supervised RF, self-supervised ensemble forest, unsupervised random forest with self-labeling	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Apparentées	6	3
Résumé	Self-supervised Random Forest (SSL-RF) extends the classic random forest to settings where labeled examples are scarce. The forest is first trained using automatically generated pseudo-labels derived from a self-supervised pretext task — such as predicting data transformations or masked features — and then refined on whatever true labels are available, marrying the label-efficiency of self-supervised learning with the robustness of ensemble trees.	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.