So sánh phương pháp

	Mạng nơ-ron tích chập bán giám sát	Phân loại ảnh bán giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2013–2017	2013–2020
Người khởi xướng	Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)	Lee, D.-H. (pseudo-label); Sohn et al. (FixMatch)
Loại	Semi-supervised deep learning	Semi-supervised deep learning
Công trình gốc	Lee, D.-H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. ICML Workshop on Challenges in Representation Learning.	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	SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN	SSL image classification, semi-supervised CNN classification, pseudo-label image classification, label-efficient image classification
Liên quan	5	5
Tóm tắt	A Semi-supervised CNN trains a convolutional network on a small labeled image set and a larger pool of unlabeled images simultaneously, using techniques such as pseudo-labeling and consistency regularization to extract supervisory signal from unlabeled data. This strategy closes much of the performance gap caused by scarce annotations without requiring additional human labeling effort.	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.