Linganisha mbinu

	Multilayer Perceptron inayobadilika na dhima	Mrejesho wa akili bandia wa akili bandia unaobadilika na kikoa
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2006–2016	2010s
Mwanzilishi	Ben-David et al.; Ganin et al.	Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs)
Aina	Domain adaptation of feedforward neural network	Domain-adaptive sequential model
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	5	6
Muhtasari	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.