Linganisha mbinu

	Transformer zinazobadilika na dhima (Domain-Adaptive Transformer - DAT)	Transformer wa Maono
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2019–2022	2021
Mwanzilishi	Various (Vaswani et al. 2017 for Transformers; domain adaptation extensions emerged 2019–2022)	Dosovitskiy, A. et al.
Aina	Pre-trained model fine-tuned with domain-shift adaptation	Transformer architecture for images (self-attention over patches)
Chanzo asilia	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.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Majina mbadala	DAT, domain-adaptive Transformer, domain adaptation with Transformers, transfer-learning Transformer	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Zinazohusiana	2	5
Muhtasari	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.	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).