Comparer des méthodes

	Classification d'images	Apprentissage par transfert pour la classification d'images
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2012 (deep CNN era); conceptual roots 1989 (LeCun)	2010–2012
Auteur d'origine	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.	Pan, S. J. & Yang, Q. (transfer learning framework); Krizhevsky, Sutskever & Hinton (deep CNN backbone)
Type	Supervised classification task	Transfer learning / supervised classification
Source fondatrice	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.	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Alias	visual classification, image recognition, CNN-based classification, visual categorization	pretrained CNN image classification, fine-tuned image classifier, domain-adapted image classifier, TL-IC
Apparentées	5	4
Résumé	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.	Transfer Learning with Image Classification reuses a deep neural network backbone — typically a CNN or Vision Transformer — pretrained on a large dataset such as ImageNet, and adapts it to classify images in a new target domain. By inheriting general visual features from the source task, the approach achieves high accuracy with far fewer labeled images than training from scratch.