Salīdzināt metodes

	Domēnam adaptīvs GRU	Adaptīvs domēna rekurentais neironu tīkls
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2016–present	2010s
Autors	Cho et al. (GRU, 2014); Ganin et al. (domain-adversarial framework, 2016)	Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs)
Tips	Sequence model with domain adaptation	Domain-adaptive sequential model
Pirmavots	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014 (pp. 1724–1734). Association for Computational Linguistics.	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.
Citi nosaukumi	DA-GRU, Domain-Adapted GRU, GRU with Domain Adaptation, Domain-Shift-Robust GRU	DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN
Saistītās	4	6
Kopsavilkums	Domain-Adaptive GRU combines the Gated Recurrent Unit architecture with domain adaptation techniques to train a sequence model on a labeled source domain and transfer it to a different but related target domain, reducing performance degradation caused by distribution shift. It is widely applied in NLP tasks such as cross-domain sentiment analysis, named entity recognition, and text classification where labeled target-domain data is scarce.	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.