Comparer des méthodes

	Réseau de neurones récurrent à adaptation de domaine	Apprentissage par transfert avec réseau neuronal récurrent
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2010s	2010 (TL survey); RNN: 1986
Auteur d'origine	Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs)	Pan, S. J. & Yang, Q. (transfer learning survey); RNN origins: Rumelhart, D. E. et al. (1986)
Type	Domain-adaptive sequential model	Transfer learning on sequence model
Source fondatrice	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.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Alias	DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN	TL-RNN, Pretrained RNN, RNN Transfer Learning, Recurrent Transfer Learning
Apparentées	6	5
Résumé	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.	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.