Compara mètodes

	Aprenentatge per transferència amb classificació d'imatges	Aprenentatge per transferència amb detecció d'objectes
Camp	Aprenentatge profund	Aprenentatge profund
Família	Machine learning	Machine learning
Any d'origen	2010–2012	2010–2014
Autor original	Pan, S. J. & Yang, Q. (transfer learning framework); Krizhevsky, Sutskever & Hinton (deep CNN backbone)	Girshick, R. et al. (R-CNN line); Pan & Yang (transfer learning framework)
Tipus	Transfer learning / supervised classification	Transfer learning / fine-tuning
Font seminal	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.
Àlies	pretrained CNN image classification, fine-tuned image classifier, domain-adapted image classifier, TL-IC	pretrained object detector, fine-tuned object detection, TL-OD, domain-adapted object detection
Relacionats	4	3
Resum	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.	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.