方法对比

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