So sánh phương pháp

	Phân loại ảnh bán giám sát	Phân loại ảnh
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2013–2020	2012 (deep CNN era); conceptual roots 1989 (LeCun)
Người khởi xướng	Lee, D.-H. (pseudo-label); Sohn et al. (FixMatch)	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.
Loại	Semi-supervised deep learning	Supervised classification task
Công trình gốc	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.	Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105.
Tên gọi khác	SSL image classification, semi-supervised CNN classification, pseudo-label image classification, label-efficient image classification	visual classification, image recognition, CNN-based classification, visual categorization
Liên quan	5	5
Tóm tắt	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.	Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles.