Compară metode

	Clasificare Explicabilă a Imaginilor	Detecția obiectelor
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2016-2017	2014–2016
Autorul original	Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME)	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)
Tip	Post-hoc explainability applied to image classifiers	Supervised deep learning (region proposal or single-shot)
Sursa seminală	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.	Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–587.
Denumiri alternative	XAI image classification, interpretable image classifier, explainable CNN, transparent image recognition	visual object detection, image object localization, region-based object detection, bounding-box detection
Înrudite	4	3
Rezumat	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.	Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-human accuracy at real-time speeds on standard benchmarks.