Krahasoni metodat

	GRU i Adaptuar ndaj Domenit	Transformer i Përshtatshëm për Domene
Fusha	Mësimi i thellë	Mësimi i thellë
Familja	Machine learning	Machine learning
Viti i origjinës	2016–present	2019–2022
Krijuesi	Cho et al. (GRU, 2014); Ganin et al. (domain-adversarial framework, 2016)	Various (Vaswani et al. 2017 for Transformers; domain adaptation extensions emerged 2019–2022)
Lloji	Sequence model with domain adaptation	Pre-trained model fine-tuned with domain-shift adaptation
Burimi themelues	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.	Ni, J., Hernandez Abrego, G., Constant, N., Ma, J., Hall, K., Cer, D., & Yang, Y. (2021). Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models. Findings of ACL 2022. arXiv:2108.08877.
Emërtime të tjera	DA-GRU, Domain-Adapted GRU, GRU with Domain Adaptation, Domain-Shift-Robust GRU	DAT, domain-adaptive Transformer, domain adaptation with Transformers, transfer-learning Transformer
Të lidhura	4	2
Përmbledhja	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 Transformer (DAT) is a Transformer-based model — such as BERT or ViT — extended with an explicit domain-alignment objective so that learned representations transfer well from a labeled source domain to a different, often unlabeled, target domain. The approach combines the powerful representation capacity of Transformers with domain adaptation techniques such as adversarial training or contrastive alignment to minimise domain shift.