方法对比
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| 可解释视觉 Transformer× | 图像分类× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2021 | 2012 (deep CNN era); conceptual roots 1989 (LeCun) |
| 提出者≠ | Chefer, H., Gur, S., & Wolf, L. (attribution framework); Dosovitskiy et al. (base ViT) | Krizhevsky, A.; Sutskever, I.; Hinton, G. E. |
| 类型≠ | Post-hoc explainability applied to Vision Transformer | Supervised classification task |
| 开创性文献≠ | 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 ↗ | Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105. link ↗ |
| 别名 | XViT, Interpretable ViT, Explainable ViT, Transparent Vision Transformer | visual classification, image recognition, CNN-based classification, visual categorization |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles. |
| ScholarGate数据集 ↗ |
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