So sánh phương pháp

	Rừng Ngẫu nhiên Tự giám sát	Lan truyền nhãn
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2012–2022	2002
Người khởi xướng	Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage)	Zhu, X. & Ghahramani, Z.
Loại	Semi-supervised ensemble (self-supervised pretext task + RF)	Graph-based semi-supervised classification
Công trình gốc	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.
Tên gọi khác	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
Liên quan	6	3
Tóm tắt	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.