Comparer des méthodes

	Perceptron multicouche adaptatif au domaine	Réseau de neurones récurrent à adaptation de domaine
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2006–2016	2010s
Auteur d'origine	Ben-David et al.; Ganin et al.	Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs)
Type	Domain adaptation of feedforward neural network	Domain-adaptive sequential model
Source fondatrice	Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010). A theory of learning from different domains. Machine Learning, 79(1–2), 151–175.	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.
Alias	DA-MLP, domain-adaptive MLP, domain-adapted feedforward network, domain adaptation with MLP	DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN
Apparentées	5	6
Résumé	A domain-adaptive multilayer perceptron (DA-MLP) is a feedforward neural network trained to learn representations that are useful across a labeled source domain and an unlabeled or differently distributed target domain. By minimizing both a task loss and a domain-discrepancy objective, the MLP generalizes to the target domain with little or no target-domain labels.	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.