Σύγκριση μεθόδων

	Semi-supervised Object Detection	Μεταφορά Μάθησης με Εντοπισμό Αντικειμένων
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2020–2021	2010–2014
Δημιουργός	Sohn et al. (STAC); Liu et al. (Unbiased Teacher)	Girshick, R. et al. (R-CNN line); Pan & Yang (transfer learning framework)
Τύπος	Semi-supervised learning for detection	Transfer learning / fine-tuning
Θεμελιώδης πηγή	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.	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Εναλλακτικές ονομασίες	SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection	pretrained object detector, fine-tuned object detection, TL-OD, domain-adapted object detection
Συναφείς	6	3
Σύνοψη	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.	Transfer learning with object detection starts from a deep neural network pretrained on a large image dataset — typically ImageNet for the backbone or COCO for the full detector — and adapts it to detect objects in a new domain. By reusing learned visual representations, it achieves strong detection accuracy with far fewer annotated images than training from scratch would require.