Comparer des méthodes

	Réseau neuronal convolutif affiné	Classification d'images
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2012–2014	2012 (deep CNN era); conceptual roots 1989 (LeCun)
Auteur d'origine	Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.
Type	Transfer learning technique (supervised fine-tuning)	Supervised classification task
Source fondatrice	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27.	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.
Alias	Fine-tuned CNN, CNN fine-tuning, CNN transfer learning with fine-tuning, adapted convolutional network	visual classification, image recognition, CNN-based classification, visual categorization
Apparentées	5	5
Résumé	Fine-tuning a CNN means starting from a network already trained on a large dataset — typically ImageNet — and continuing training on a smaller target dataset so the model adapts its learned visual features to a new task. This approach dramatically reduces the data and compute required to reach strong performance compared with training from scratch.	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.