Comparer des méthodes

	Apprentissage par transfert avec détection d'objets	Apprentissage par transfert pour la classification d'images
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2010–2014	2010–2012
Auteur d'origine	Girshick, R. et al. (R-CNN line); Pan & Yang (transfer learning framework)	Pan, S. J. & Yang, Q. (transfer learning framework); Krizhevsky, Sutskever & Hinton (deep CNN backbone)
Type	Transfer learning / fine-tuning	Transfer learning / supervised classification
Source fondatrice	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Alias	pretrained object detector, fine-tuned object detection, TL-OD, domain-adapted object detection	pretrained CNN image classification, fine-tuned image classifier, domain-adapted image classifier, TL-IC
Apparentées	3	4
Résumé	Transfer learning with object detection starts from a deep neural network pretrained on a large image dataset — typically ImageNet for the backbone or COCO for the full detector — and adapts it to detect objects in a new domain. By reusing learned visual representations, it achieves strong detection accuracy with far fewer annotated images than training from scratch would require.	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.