Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Vision Transformer auto-supervisé× | Vision Transformer× | |
|---|---|---|
| Domaine | Apprentissage profond | Apprentissage profond |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2021–2022 | 2021 |
| Auteur d'origine≠ | 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) |
| Source fondatrice≠ | 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 ↗ |
| Alias | 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 |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | 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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