Porovnat metody

	Logistická regrese se samoučením	Samoučené rozhodovací stromy
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2020s	2015–present
Tvůrce	Chen et al. (SimCLR linear evaluation protocol, 2020); logistic probe practice widely adopted across SSL literature	Multiple authors (active research area, 2010s–2020s)
Typ	Self-supervised pretraining + supervised linear classification	Self-supervised ensemble/single tree model
Původní zdroj	Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML), 1597–1607.	Self-supervised learning. Wikipedia.
Další názvy	SSL linear probe, contrastive pretraining with logistic classifier, self-supervised linear evaluation, SSL + logistic regression	SSL decision tree, self-supervised tree classifier, pseudo-label decision tree, unsupervised-guided decision tree
Příbuzné	5	5
Shrnutí	Self-supervised logistic regression is a two-stage pipeline in which a neural encoder is first trained on abundant unlabeled data through a self-supervised pretext task — such as contrastive learning or masked prediction — and then the frozen learned representations are classified with a standard logistic regression model trained on a small labeled dataset. This linear evaluation protocol is widely used to benchmark the quality of self-supervised representations.	Self-supervised Decision Tree learning combines the interpretability of classical decision trees with the ability to exploit large quantities of unlabeled data through self-supervised pretext tasks. The model learns useful feature representations or node-split criteria from unlabeled samples before refining predictions on a small labeled set, bridging the gap between fully supervised trees and purely unsupervised clustering.