Linganisha mbinu

	Ugawaji wa Kisemantiki wa Nusu-Usimamizi	Mtandao wa Mawasiliano wa Nusu-Usindikaji
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2018–2020	2013–2017
Mwanzilishi	Multiple (Ouali et al., Zou et al., Chen et al.)	Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)
Aina	Semi-supervised deep learning for pixel-level classification	Semi-supervised deep learning
Chanzo asilia	Ouali, Y., Hudelot, C., & Tami, M. (2020). Semi-Supervised Semantic Segmentation with Cross-Consistency Training. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12674–12684.	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.
Majina mbadala	Semi-SSL segmentation, pseudo-label segmentation, consistency regularization segmentation, label-efficient semantic segmentation	SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN
Zinazohusiana	5	5
Muhtasari	Semi-supervised semantic segmentation trains pixel-level labeling models using a small set of fully labeled images combined with a much larger set of unlabeled images. Techniques such as pseudo-labeling and consistency regularization extract supervisory signal from unlabeled data, making it possible to achieve near-fully-supervised accuracy at a fraction of the annotation cost.	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.