Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Адаптивний до домену трансформер для зорових даних× | Трансформер для комп'ютерного зору× | |
|---|---|---|
| Галузь | Глибоке навчання | Глибоке навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2021–2023 | 2021 |
| Автор методу≠ | Multiple groups (Yang et al., 2023; Xu et al., 2021; Ma et al., 2022) | Dosovitskiy, A. et al. |
| Тип≠ | Domain adaptation + Vision Transformer ensemble | Transformer architecture for images (self-attention over patches) |
| Основоположне джерело≠ | 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). link ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Інші назви | DA-ViT, Domain Adaptation with Vision Transformer, ViT with Domain Adaptation, Domain-Adaptive ViT | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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). |
| ScholarGateНабір даних ↗ |
|
|