Machine learningDeep learning / NLP / CV
域自适应 Transformer
域自适应 Transformer (DAT) 是一种基于 Transformer 的模型——例如 BERT 或 ViT——并增加了一个显式的域对齐目标，以便学习到的表征能从有标签的源域很好地迁移到不同的、通常是无标签的目标域。该方法结合了 Transformer 强大的表征能力与域自适应技术（如对抗训练或对比对齐），以最小化域偏移。
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来源
- 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. link ↗
- Guo, J., Shah, D., & Barzilay, R. (2022). Multi-Source Domain Adaptation with Mixture of Experts. In Proceedings of EMNLP 2018. arXiv:1809.02060. link ↗
如何引用本页
ScholarGate. (2026, June 3). Domain-Adaptive Transformer (DAT). ScholarGate. https://scholargate.app/zh/deep-learning/domain-adaptive-transformer
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