Comparar métodos

	Red Neuronal Recurrente Adaptativa al Dominio	Transformador de Adaptación de Dominio
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2010s	2019–2022
Autor original	Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs)	Various (Vaswani et al. 2017 for Transformers; domain adaptation extensions emerged 2019–2022)
Tipo	Domain-adaptive sequential model	Pre-trained model fine-tuned with domain-shift adaptation
Fuente seminal	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.	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.
Alias	DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN	DAT, domain-adaptive Transformer, domain adaptation with Transformers, transfer-learning Transformer
Relacionados	6	2
Resumen	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.	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.