Linganisha mbinu

	Uainishaji wa Picha kwa Usimamizi Dhaifu	Kujifunza kwa Kuhamisha kwa Uainishaji wa Picha
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2014–2016	2010–2012
Mwanzilishi	Multiple contributors; class activation map approach: Zhou et al.	Pan, S. J. & Yang, Q. (transfer learning framework); Krizhevsky, Sutskever & Hinton (deep CNN backbone)
Aina	Weakly supervised deep learning paradigm	Transfer learning / supervised classification
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	5	4
Muhtasari	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.