方法对比

	领域自适应多层感知器	域自适应 Transformer
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2006–2016	2019–2022
提出者	Ben-David et al.; Ganin et al.	Various (Vaswani et al. 2017 for Transformers; domain adaptation extensions emerged 2019–2022)
类型	Domain adaptation of feedforward neural network	Pre-trained model fine-tuned with domain-shift adaptation
开创性文献	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.	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.
别名	DA-MLP, domain-adaptive MLP, domain-adapted feedforward network, domain adaptation with MLP	DAT, domain-adaptive Transformer, domain adaptation with Transformers, transfer-learning Transformer
相关	5	2
摘要	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 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.