方法对比

	弱监督图像分类	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	2014–2016	2010–2012
提出者	Multiple contributors; class activation map approach: Zhou et al.	Pan, S. J. & Yang, Q. (transfer learning framework); Krizhevsky, Sutskever & Hinton (deep CNN backbone)
类型	Weakly supervised deep learning paradigm	Transfer learning / supervised classification
开创性文献	Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning Deep Features for Discriminative Localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929.	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
别名	WSL image classification, image-level supervised classification, noisy-label image classification, weakly labeled visual recognition	pretrained CNN image classification, fine-tuned image classifier, domain-adapted image classifier, TL-IC
相关	5	4
摘要	Weakly supervised image classification trains convolutional or transformer-based networks using only coarse, incomplete, or noisy supervision — such as image-level category labels, hashtags, or web-scraped tags — without requiring precise bounding boxes or pixel annotations. This dramatically reduces labeling cost while still enabling high-accuracy visual recognition at scale.	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.