השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| Vision Transformer בר-הסבר× | טרנספורמר ראייה× | |
|---|---|---|
| תחום | למידה עמוקה | למידה עמוקה |
| משפחה | Machine learning | Machine learning |
| שנת המקור | 2021 | 2021 |
| הוגה השיטה≠ | Chefer, H., Gur, S., & Wolf, L. (attribution framework); Dosovitskiy et al. (base ViT) | Dosovitskiy, A. et al. |
| סוג≠ | Post-hoc explainability applied to Vision Transformer | Transformer architecture for images (self-attention over patches) |
| מקור מכונן≠ | Chefer, H., Gur, S., & Wolf, L. (2021). Transformer interpretability beyond attention visualization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 782–791. DOI ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| כינויים | XViT, Interpretable ViT, Explainable ViT, Transparent Vision Transformer | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| קשורות | 5 | 5 |
| תקציר≠ | Explainable Vision Transformer combines the strong image-recognition performance of Vision Transformers (ViT) with attribution techniques — such as relevance propagation, attention rollout, or gradient-weighted attention — that highlight which image regions drive each prediction. The approach enables researchers and practitioners to audit model decisions and satisfy transparency requirements without sacrificing accuracy. | 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). |
| ScholarGateמערך נתונים ↗ |
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