Comparar métodos

	Regressão Logística Autossupervisionada	Regressão Logística Semissupervisionada
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2020s	1995–2000
Autor original	Chen et al. (SimCLR linear evaluation protocol, 2020); logistic probe practice widely adopted across SSL literature	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)
Tipo	Self-supervised pretraining + supervised linear classification	Semi-supervised classifier
Fonte seminal	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.	Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134.
Outros nomes	SSL linear probe, contrastive pretraining with logistic classifier, self-supervised linear evaluation, SSL + logistic regression	SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier
Relacionados	5	5
Resumo	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.	Semi-supervised logistic regression extends the standard logistic classifier by incorporating unlabeled data during training. Using self-training, expectation-maximization, or label-propagation wrappers, it iteratively assigns soft labels to unlabeled examples and refines model parameters, improving generalization when labeled data are scarce relative to the full dataset.