Comparer des méthodes

	Détection d'objets semi-supervisée	CNN convolutive semi-supervisée
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2020–2021	2013–2017
Auteur d'origine	Sohn et al. (STAC); Liu et al. (Unbiased Teacher)	Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)
Type	Semi-supervised learning for detection	Semi-supervised deep learning
Source fondatrice	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 Workshop on Challenges in Representation Learning.
Alias	SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection	SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN
Apparentées	6	5
Résumé	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.	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.