方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 可解释实例分割× | 可解释图像分类× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017–present | 2016-2017 |
| 提出者≠ | He, K. et al. (Mask R-CNN); XAI extensions by multiple authors | Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME) |
| 类型≠ | Explainability-augmented deep learning pipeline | Post-hoc explainability applied to image classifiers |
| 开创性文献≠ | Lindner, M., Meng, C., & Bischl, B. (2023). Explaining Instance Segmentation Models via Saliency Maps and Occlusion. IEEE Transactions on Pattern Analysis and Machine Intelligence. link ↗ | 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 ↗ |
| 别名 | XAI instance segmentation, interpretable instance segmentation, transparent mask prediction, explainable Mask R-CNN | XAI image classification, interpretable image classifier, explainable CNN, transparent image recognition |
| 相关≠ | 6 | 4 |
| 摘要≠ | Explainable Instance Segmentation combines deep-learning instance segmentation models — which detect and delineate every individual object as a separate pixel mask — with post-hoc or ante-hoc explainability techniques such as GradCAM, SHAP, LIME, or attention visualization, so that each predicted mask is accompanied by evidence showing which image regions drove the model's decision. | 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. |
| ScholarGate数据集 ↗ |
|
|