方法对比
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| 可解释图像分类× | 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≠ | 2016-2017 | 2012 (deep CNN era); conceptual roots 1989 (LeCun) |
| 提出者≠ | Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME) | Krizhevsky, A.; Sutskever, I.; Hinton, G. E. |
| 类型≠ | Post-hoc explainability applied to image classifiers | Supervised classification task |
| 开创性文献≠ | Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618-626. 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 | XAI image classification, interpretable image classifier, explainable CNN, transparent image recognition | visual classification, image recognition, CNN-based classification, visual categorization |
| 相关≠ | 4 | 5 |
| 摘要≠ | Explainable image classification combines a deep learning image classifier — typically a CNN or Vision Transformer — with a post-hoc or intrinsic interpretability method such as Grad-CAM, LIME, or SHAP to produce visual or quantitative explanations of why the model assigned a particular label to an image. The goal is to make the classifier's decision process transparent, auditable, and trustworthy. | 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. |
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