Machine learningDeep learning / NLP / CV
可解释图像分类
可解释图像分类将深度学习图像分类器——通常是卷积神经网络（CNN）或视觉Transformer——与事后或内在的可解释性方法（如Grad-CAM、LIME或SHAP）相结合，以生成关于模型为何将特定标签分配给图像的视觉或量化解释。目标是使分类器的决策过程透明、可审计且值得信赖。
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来源
- 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: 10.1109/ICCV.2017.74 ↗
- Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why Should I Trust You?: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135-1144. DOI: 10.1145/2939672.2939778 ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Image Classification (XAI-augmented CNN/Transformer Classifiers). ScholarGate. https://scholargate.app/zh/deep-learning/explainable-image-classification
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