So sánh phương pháp

	Phân loại ảnh với giám sát yếu	Phân loại ảnh bán giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2014–2016	2013–2020
Người khởi xướng	Multiple contributors; class activation map approach: Zhou et al.	Lee, D.-H. (pseudo-label); Sohn et al. (FixMatch)
Loại	Weakly supervised deep learning paradigm	Semi-supervised deep learning
Công trình gốc	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.	Lee, D.-H. (2013). Pseudo-Label: The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks. ICML 2013 Workshop on Challenges in Representation Learning.
Tên gọi khác	WSL image classification, image-level supervised classification, noisy-label image classification, weakly labeled visual recognition	SSL image classification, semi-supervised CNN classification, pseudo-label image classification, label-efficient image classification
Liên quan	5	5
Tóm tắt	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.	Semi-supervised image classification trains deep neural networks on a small set of labeled images together with a much larger pool of unlabeled images. Techniques such as pseudo-labeling, consistency regularization, and confidence thresholding allow the model to leverage the structure of unlabeled data, dramatically reducing the need for expensive manual annotation while approaching fully-supervised accuracy.