Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Apprentissage par transfert avec réseau neuronal récurrent× | Long Short-Term Memory (LSTM)× | |
|---|---|---|
| Domaine | Apprentissage profond | Apprentissage profond |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2010 (TL survey); RNN: 1986 | 1997 |
| Auteur d'origine≠ | Pan, S. J. & Yang, Q. (transfer learning survey); RNN origins: Rumelhart, D. E. et al. (1986) | Hochreiter, S. & Schmidhuber, J. |
| Type≠ | Transfer learning on sequence model | Recurrent neural network with gated memory cells |
| Source fondatrice≠ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| Alias | TL-RNN, Pretrained RNN, RNN Transfer Learning, Recurrent Transfer Learning | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | Transfer Learning with Recurrent Neural Network (TL-RNN) reuses weights learned by an RNN on a large source task — such as language modelling or sequence prediction — and adapts them to a new, often smaller target task. This strategy lets practitioners obtain strong sequence-modelling performance without the need for massive labelled datasets. | 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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