Comparer des méthodes

	Classification d'images adaptative au domaine	Classification d'images
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2015–2016	2012 (deep CNN era); conceptual roots 1989 (LeCun)
Auteur d'origine	Ganin, Y. & Lempitsky, V. (domain-adversarial formulation)	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.
Type	Domain adaptation / transfer learning	Supervised classification task
Source fondatrice	Ganin, Y., Ustunova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35.	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	domain adaptation for image classification, DAIC, cross-domain image classification, domain-shift-robust image recognition	visual classification, image recognition, CNN-based classification, visual categorization
Apparentées	3	5
Résumé	Domain-adaptive image classification trains a visual classifier on a labeled source domain and adapts it to a target domain where labeled data are scarce or absent. By aligning feature distributions across domains, the model retains discriminative accuracy on the target distribution without requiring full target re-annotation, making it practical in real-world deployment scenarios where domain shift is unavoidable.	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.