So sánh phương pháp

	Phân loại ảnh bán giám sát	Transfer Learning với Phân loại Ảnh
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2013–2020	2010–2012
Người khởi xướng	Lee, D.-H. (pseudo-label); Sohn et al. (FixMatch)	Pan, S. J. & Yang, Q. (transfer learning framework); Krizhevsky, Sutskever & Hinton (deep CNN backbone)
Loại	Semi-supervised deep learning	Transfer learning / supervised classification
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.	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Tên gọi khác	SSL image classification, semi-supervised CNN classification, pseudo-label image classification, label-efficient image classification	pretrained CNN image classification, fine-tuned image classifier, domain-adapted image classifier, TL-IC
Liên quan	5	4
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.	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.