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| Προσαρμοστικό σε πεδία Επαναλαμβανόμενο Νευρωνικό Δίκτυο× | Μνήμη Μακράς Βραχείας Διάρκειας (LSTM)× | |
|---|---|---|
| Πεδίο | Βαθιά Μάθηση | Βαθιά Μάθηση |
| Οικογένεια | Machine learning | Machine learning |
| Έτος προέλευσης≠ | 2010s | 1997 |
| Δημιουργός≠ | Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs) | Hochreiter, S. & Schmidhuber, J. |
| Τύπος≠ | Domain-adaptive sequential model | Recurrent neural network with gated memory cells |
| Θεμελιώδης πηγή≠ | Ganin, Y., Ustunova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35. link ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| Εναλλακτικές ονομασίες | DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN |
| Συναφείς≠ | 6 | 4 |
| Σύνοψη≠ | A Domain-adaptive Recurrent Neural Network (DA-RNN) is a recurrent neural network trained on a source domain and adapted to a target domain using domain adaptation techniques such as adversarial training, feature alignment, or fine-tuning. It enables sequential models to generalise across domains when labeled target-domain data is scarce or unavailable. | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
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