Linganisha mbinu

	Ugunduzi wa Kina wa Maelezo (Explainable Object Detection)	Uainishaji wa Picha unaoelezeka
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2016–2017	2016-2017
Mwanzilishi	Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME); Lundberg & Lee (SHAP)	Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME)
Aina	Post-hoc explainability applied to object detection	Post-hoc explainability applied to image classifiers
Chanzo asilia	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.	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.
Majina mbadala	XAI Object Detection, Interpretable Object Detection, Transparent Object Detection, Explainable OD	XAI image classification, interpretable image classifier, explainable CNN, transparent image recognition
Zinazohusiana	5	4
Muhtasari	Explainable object detection combines a deep-learning object detector — such as YOLO, Faster R-CNN, or DETR — with post-hoc or built-in explainability methods (Grad-CAM, LIME, SHAP, D-RISE) that visualize why the model placed a bounding box at a particular location and assigned a particular class label, making its decisions auditable by humans.	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.