Сравнение на методи

	Трансферно обучение с класификация на изображения	Трансферно обучение с детекция на обекти
Област	Дълбоко обучение	Дълбоко обучение
Семейство	Machine learning	Machine learning
Година на възникване	2010–2012	2010–2014
Създател	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)
Тип	Transfer learning / supervised classification	Transfer learning / fine-tuning
Основополагащ източник	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.
Други названия	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
Свързани	4	3
Резюме	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.