Linganisha mbinu

	Ugunduzi wa Kitu kwa Njia ya Kujifundisha	Uchanganuzi Semi-Nusu-Jitoleaji wa Vitu
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2019–2021	2020–2021
Mwanzilishi	He et al. (MoCo); Caron et al. (DINO); Henaff et al. (DetCon)	Sohn et al. (STAC); Liu et al. (Unbiased Teacher)
Aina	Self-supervised pre-training + supervised fine-tuning	Semi-supervised learning for detection
Chanzo asilia	He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9729–9738.	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.
Majina mbadala	SSL object detection, self-supervised detection, unsupervised pre-training for detection, contrastive pre-training for detection	SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection
Zinazohusiana	4	6
Muhtasari	Self-supervised object detection uses unlabeled image data to pre-train a visual backbone through pretext tasks such as contrastive learning or masked image modeling, then fine-tunes the backbone with a detection head on a smaller labeled dataset. This approach dramatically reduces reliance on expensive bounding-box annotations while matching or approaching fully supervised detection performance.	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.