Comparer des méthodes

	Classification d'images adaptative au domaine	Classification d'images par réglage fin
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2015–2016	2010–2014
Auteur d'origine	Ganin, Y. & Lempitsky, V. (domain-adversarial formulation)	Yosinski, J. et al.; Pan, S. J. & Yang, Q.
Type	Domain adaptation / transfer learning	Transfer learning / fine-tuning
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.	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems (NeurIPS), 27, 3320–3328.
Alias	domain adaptation for image classification, DAIC, cross-domain image classification, domain-shift-robust image recognition	fine-tuning for image recognition, transfer learning image classifier, pretrained CNN fine-tuning, domain-specific image classifier
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.	Fine-tuned image classification adapts a large neural network pretrained on a broad image corpus (such as ImageNet) to a specific target domain by continuing training on labeled domain images. This approach achieves strong accuracy with far fewer target-domain samples than training from scratch, making it the dominant paradigm for applied computer vision tasks.