방법 비교

	Self-supervised Logistic Regression	자가 지도 결정 트리
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2020s	2015–present
창시자	Chen et al. (SimCLR linear evaluation protocol, 2020); logistic probe practice widely adopted across SSL literature	Multiple authors (active research area, 2010s–2020s)
유형	Self-supervised pretraining + supervised linear classification	Self-supervised ensemble/single tree model
원전	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.
별칭	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
관련	5	5
요약	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.