পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| ডোমেইন-অ্যাডাপ্টিভ ট্রান্সফরমার× | ভিশন ট্রান্সফরমার× | |
|---|---|---|
| ক্ষেত্র | গভীর শিখন | গভীর শিখন |
| পরিবার | Machine learning | Machine learning |
| উদ্ভবের বছর≠ | 2019–2022 | 2021 |
| প্রবর্তক≠ | Various (Vaswani et al. 2017 for Transformers; domain adaptation extensions emerged 2019–2022) | Dosovitskiy, A. et al. |
| ধরন≠ | Pre-trained model fine-tuned with domain-shift adaptation | Transformer architecture for images (self-attention over patches) |
| মৌলিক উৎস≠ | 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 ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| অপর নাম | DAT, domain-adaptive Transformer, domain adaptation with Transformers, transfer-learning Transformer | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| সম্পর্কিত≠ | 2 | 5 |
| সারসংক্ষেপ≠ | 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). |
| ScholarGateডেটাসেট ↗ |
|
|