Comparer des méthodes

	Forêt aléatoire auto-supervisée	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2012–2022	2001
Auteur d'origine	Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage)	Breiman, L.
Type	Semi-supervised ensemble (self-supervised pretext task + RF)	Ensemble (bagging of decision trees)
Source fondatrice	Lefortier, D., Chitta, K., & Agrawal, P. (2022). Self-supervised random forests. arXiv:2204.01430.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	SSL-RF, self-supervised RF, self-supervised ensemble forest, unsupervised random forest with self-labeling	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	6	4
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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.