방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| Self-supervised Vision Transformer× | Vision Transformer× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2021–2022 | 2021 |
| 창시자≠ | Caron et al. (DINO); He et al. (MAE) | Dosovitskiy, A. et al. |
| 유형≠ | Self-supervised pre-training for vision transformers | Transformer architecture for images (self-attention over patches) |
| 원전≠ | 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 ↗ |
| 별칭 | 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 |
| 관련≠ | 4 | 5 |
| 요약≠ | 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). |
| ScholarGate데이터셋 ↗ |
|
|