방법 비교

	도메인 적응형 비전 트랜스포머	Vision Transformer
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2021–2023	2021
창시자	Multiple groups (Yang et al., 2023; Xu et al., 2021; Ma et al., 2022)	Dosovitskiy, A. et al.
유형	Domain adaptation + Vision Transformer ensemble	Transformer architecture for images (self-attention over patches)
원전	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).	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
별칭	DA-ViT, Domain Adaptation with Vision Transformer, ViT with Domain Adaptation, Domain-Adaptive ViT	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
관련	5	5
요약	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.	The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).