เปรียบเทียบวิธี

	วิทัศน์ทรานส์ฟอร์มปรับโดเมน (Domain-Adaptive Vision Transformer - DA-ViT)	การจำแนกประเภทแบบ BERT ที่ปรับให้เข้ากับโดเมน
สาขาวิชา	การเรียนรู้เชิงลึก	การเรียนรู้เชิงลึก
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2021–2023	2019–2020
ผู้ริเริ่ม	Multiple groups (Yang et al., 2023; Xu et al., 2021; Ma et al., 2022)	Gururangan et al. (2020); earlier domain-specific instances include Lee et al. (2020) — BioBERT
ประเภท	Domain adaptation + Vision Transformer ensemble	Domain-adaptive pre-training followed by supervised fine-tuning
แหล่งต้นตำรับ	Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations (ICLR).	Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 8342–8360.
ชื่อเรียกอื่น	DA-ViT, Domain Adaptation with Vision Transformer, ViT with Domain Adaptation, Domain-Adaptive ViT	DAPT BERT classification, domain-adaptive pre-training, domain-specific BERT fine-tuning, BERT DAPT
ที่เกี่ยวข้อง	5	6
สรุป	Domain-Adaptive Vision Transformer (DA-ViT) applies domain adaptation techniques — such as adversarial alignment, self-training, or attention-level bridging — on top of a pretrained Vision Transformer backbone to transfer visual knowledge from a labeled source domain to an unlabeled or lightly labeled target domain, reducing the distribution shift that limits standard ViT fine-tuning.	Domain-adaptive BERT-based classification extends the standard fine-tuning pipeline by first continuing BERT's masked-language-model pre-training on a large corpus of in-domain unlabeled text, then fine-tuning the adapted model on labeled examples for the target classification task. This two-stage approach closes the vocabulary and distributional gap between BERT's general pre-training corpus and specialized domains such as biomedicine, law, finance, or social-media text.