Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| GRU semi-supervisé× | Long Short-Term Memory (LSTM)× | |
|---|---|---|
| Domaine | Apprentissage profond | Apprentissage profond |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2014–2015 | 1997 |
| Auteur d'origine≠ | Dai, A. M. & Le, Q. V. (semi-supervised sequence learning); Cho, K. et al. (GRU architecture) | Hochreiter, S. & Schmidhuber, J. |
| Type≠ | Semi-supervised sequence model | Recurrent neural network with gated memory cells |
| Source fondatrice≠ | Dai, A. M., & Le, Q. V. (2015). Semi-supervised Sequence Learning. Advances in Neural Information Processing Systems (NeurIPS), 28. link ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| Alias | Semi-supervised GRU, SSL-GRU, GRU with unlabeled data, semi-supervised recurrent classifier | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN |
| Apparentées≠ | 5 | 4 |
| 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. | Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step. |
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