Comparer des méthodes

	GRU semi-supervisé	LSTM semi-supervisé
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2014–2015	2015–2018
Auteur d'origine	Dai, A. M. & Le, Q. V. (semi-supervised sequence learning); Cho, K. et al. (GRU architecture)	Hochreiter, S. & Schmidhuber, J. (LSTM); semi-supervised extensions by various authors (2015–2020)
Type	Semi-supervised sequence model	Semi-supervised sequence model
Source fondatrice	Dai, A. M., & Le, Q. V. (2015). Semi-supervised Sequence Learning. Advances in Neural Information Processing Systems (NeurIPS), 28.	Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
Alias	Semi-supervised GRU, SSL-GRU, GRU with unlabeled data, semi-supervised recurrent classifier	SSL-LSTM, semi-supervised sequence model, LSTM with unlabeled data, pseudo-label LSTM
Apparentées	5	3
Résumé	Semi-supervised GRU applies the Gated Recurrent Unit architecture to settings where only a small fraction of sequential data is labeled. By first pre-training or jointly training on abundant unlabeled sequences — through language modeling, auto-encoding, or consistency regularization — and then fine-tuning on labeled examples, the model exploits the full corpus to learn richer sequence representations than supervised-only training would allow.	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.