So sánh phương pháp

	Phát hiện đối tượng bán giám sát	Phân loại ảnh bán giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2020–2021	2013–2020
Người khởi xướng	Sohn et al. (STAC); Liu et al. (Unbiased Teacher)	Lee, D.-H. (pseudo-label); Sohn et al. (FixMatch)
Loại	Semi-supervised learning for detection	Semi-supervised deep learning
Công trình gốc	Sohn, K., Zhang, Z., Li, C.-L., Zhang, H., Lee, C.-Y., & Pfister, T. (2020). A Simple Semi-Supervised Learning Framework for Object Detection. arXiv preprint arXiv:2005.04757.	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	SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection	SSL image classification, semi-supervised CNN classification, pseudo-label image classification, label-efficient image classification
Liên quan	6	5
Tóm tắt	Semi-supervised object detection trains a detector on a small labeled image set and a large unlabeled image set. A teacher model generates pseudo-labels for unlabeled images, and a student model learns from both real and pseudo-labeled data, dramatically reducing the expensive manual bounding-box annotation burden while achieving accuracy competitive with fully supervised baselines.	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.