Comparer des méthodes

	Classification d'images explicable	Segmentation sémantique
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2016-2017	2015
Auteur d'origine	Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME)	Long, J., Shelhamer, E., & Darrell, T.
Type	Post-hoc explainability applied to image classifiers	Dense prediction / pixel-wise classification
Source fondatrice	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.	Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440.
Alias	XAI image classification, interpretable image classifier, explainable CNN, transparent image recognition	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation
Apparentées	4	5
Résumé	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.	Semantic segmentation assigns a class label to every pixel in an image, producing a dense, category-annotated map of the scene. Unlike object detection, which draws bounding boxes, it delineates the exact spatial extent of each class, making it indispensable in medical imaging, autonomous driving, satellite analysis, and any task where precise region boundaries matter.