قارن الطرق

	اكتشاف الكائنات شبه المُشرف عليه	تصنيف الصور شبه المُشرف عليه
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2020–2021	2013–2020
صاحب الطريقة	Sohn et al. (STAC); Liu et al. (Unbiased Teacher)	Lee, D.-H. (pseudo-label); Sohn et al. (FixMatch)
النوع	Semi-supervised learning for detection	Semi-supervised deep learning
المصدر التأسيسي	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.
الأسماء البديلة	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
ذات صلة	6	5
الملخص	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.