Compara mètodes

	GRU adaptatiu al domini	Unitat recurrent amb portes (GRU)
Camp	Aprenentatge profund	Aprenentatge profund
Família	Machine learning	Machine learning
Any d'origen	2016–present	2014
Autor original	Cho et al. (GRU, 2014); Ganin et al. (domain-adversarial framework, 2016)	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.
Tipus	Sequence model with domain adaptation	Recurrent neural network with gating
Font seminal	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.	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.
Àlies	DA-GRU, Domain-Adapted GRU, GRU with Domain Adaptation, Domain-Shift-Robust GRU	GRU, GRU network, gated RNN, GRU cell
Relacionats	4	3
Resum	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.	The Gated Recurrent Unit (GRU), introduced by Cho et al. in 2014, is a streamlined recurrent neural network that uses two learned gates — an update gate and a reset gate — to selectively retain or discard information across time steps, enabling effective sequence modelling with fewer parameters than LSTM.