Compara mètodes

	Aprenentatge per transferència amb detecció d'objectes	Aprenentatge per transferència amb classificació d'imatges
Camp	Aprenentatge profund	Aprenentatge profund
Família	Machine learning	Machine learning
Any d'origen	2010–2014	2010–2012
Autor original	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)
Tipus	Transfer learning / fine-tuning	Transfer learning / supervised classification
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 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
Relacionats	3	4
Resum	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.