Linganisha mbinu

	Uainishaji wa Picha kwa Usimamizi Dhaifu	Uainishaji wa Picha Ulioboreshwa
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2014–2016	2010–2014
Mwanzilishi	Multiple contributors; class activation map approach: Zhou et al.	Yosinski, J. et al.; Pan, S. J. & Yang, Q.
Aina	Weakly supervised deep learning paradigm	Transfer learning / fine-tuning
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.	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems (NeurIPS), 27, 3320–3328.
Majina mbadala	WSL image classification, image-level supervised classification, noisy-label image classification, weakly labeled visual recognition	fine-tuning for image recognition, transfer learning image classifier, pretrained CNN fine-tuning, domain-specific image classifier
Zinazohusiana	5	5
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.	Fine-tuned image classification adapts a large neural network pretrained on a broad image corpus (such as ImageNet) to a specific target domain by continuing training on labeled domain images. This approach achieves strong accuracy with far fewer target-domain samples than training from scratch, making it the dominant paradigm for applied computer vision tasks.