Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Forklarlig Vision Transformer× | Vision Transformer× | |
|---|---|---|
| Fagområde | Dyb læring | Dyb læring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår | 2021 | 2021 |
| Ophavsperson≠ | Chefer, H., Gur, S., & Wolf, L. (attribution framework); Dosovitskiy et al. (base ViT) | Dosovitskiy, A. et al. |
| Type≠ | Post-hoc explainability applied to Vision Transformer | Transformer architecture for images (self-attention over patches) |
| Oprindelig kilde≠ | 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 ↗ |
| Aliasser | XViT, Interpretable ViT, Explainable ViT, Transparent Vision Transformer | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Relaterede | 5 | 5 |
| Resumé≠ | 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). |
| ScholarGateDatasæt ↗ |
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