قارن الطرق

	التعلم بالنقل مع كشف الكائنات	التعلم بالنقل لتصنيف الصور
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2010–2014	2010–2012
صاحب الطريقة	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)
النوع	Transfer learning / fine-tuning	Transfer learning / supervised classification
المصدر التأسيسي	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.
الأسماء البديلة	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
ذات صلة	3	4
الملخص	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.