Comparer des méthodes

	Perceptron multicouche semi-supervisé	LSTM semi-supervisé
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2006–2013	2015–2018
Auteur d'origine	Chapelle, O.; Scholkopf, B.; Zien, A. (eds.); Lee, D.-H.	Hochreiter, S. & Schmidhuber, J. (LSTM); semi-supervised extensions by various authors (2015–2020)
Type	Semi-supervised feedforward neural network	Semi-supervised sequence model
Source fondatrice	Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
Alias	SSL-MLP, semi-supervised MLP, semi-supervised feedforward network, partially supervised multilayer perceptron	SSL-LSTM, semi-supervised sequence model, LSTM with unlabeled data, pseudo-label LSTM
Apparentées	4	3
Résumé	A semi-supervised multilayer perceptron (SSL-MLP) is a feedforward neural network trained on a small pool of labeled examples together with a larger pool of unlabeled examples. By combining supervised cross-entropy loss on labeled data with an unsupervised consistency or pseudo-label objective on unlabeled data, it extracts far more signal from the data than a purely supervised MLP trained on labels alone.	Semi-supervised LSTM combines the sequential memory of Long Short-Term Memory networks with semi-supervised learning strategies — using a small labeled dataset alongside a large pool of unlabeled sequences. The model is pretrained or regularized on unlabeled data, then fine-tuned on labeled examples, delivering strong generalization when labeled data is scarce.