Сравнение методов

	Объяснимая классификация изображений	Классификация изображений
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2016-2017	2012 (deep CNN era); conceptual roots 1989 (LeCun)
Автор метода	Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME)	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.
Тип	Post-hoc explainability applied to image classifiers	Supervised classification task
Основополагающий источник	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.	Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105.
Другие названия	XAI image classification, interpretable image classifier, explainable CNN, transparent image recognition	visual classification, image recognition, CNN-based classification, visual categorization
Связанные	4	5
Сводка	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.	Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles.