Comparar métodos
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| Transformador de Adaptación de Dominio× | Vision Transformer× | |
|---|---|---|
| Campo | Aprendizaje profundo | Aprendizaje profundo |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2019–2022 | 2021 |
| Autor original≠ | Various (Vaswani et al. 2017 for Transformers; domain adaptation extensions emerged 2019–2022) | Dosovitskiy, A. et al. |
| Tipo≠ | Pre-trained model fine-tuned with domain-shift adaptation | Transformer architecture for images (self-attention over patches) |
| Fuente seminal≠ | 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 ↗ |
| Alias | DAT, domain-adaptive Transformer, domain adaptation with Transformers, transfer-learning Transformer | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Relacionados≠ | 2 | 5 |
| Resumen≠ | 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). |
| ScholarGateConjunto de datos ↗ |
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