Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Selv-overvåget Vision Transformer× | Vision Transformer× | |
|---|---|---|
| Fagområde | Dyb læring | Dyb læring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2021–2022 | 2021 |
| Ophavsperson≠ | Caron et al. (DINO); He et al. (MAE) | Dosovitskiy, A. et al. |
| Type≠ | Self-supervised pre-training for vision transformers | Transformer architecture for images (self-attention over patches) |
| Oprindelig kilde≠ | Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660. link ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Aliasser | SSL-ViT, self-supervised ViT, unsupervised ViT pre-training, vision transformer self-supervised pre-training | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Relaterede≠ | 4 | 5 |
| Resumé≠ | Self-supervised Vision Transformer (SSL-ViT) applies self-supervised pre-training objectives — such as masked patch prediction (MAE) or self-distillation with no labels (DINO) — to the Vision Transformer architecture, enabling powerful visual representations to be learned from large unlabeled image corpora before any task-specific 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). |
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