方法对比

	基于对象检测的迁移学习	迁移学习在图像分类中的应用
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2010–2014	2010–2012
提出者	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)
类型	Transfer learning / fine-tuning	Transfer learning / supervised classification
开创性文献	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.
别名	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
相关	3	4
摘要	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.