Bandingkan kaedah

	Segmentasi Semantik Semi-Terawasi	Rangkaian Saraf Konvolusional Separuh-Terawasi
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2018–2020	2013–2017
Pengasas	Multiple (Ouali et al., Zou et al., Chen et al.)	Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)
Jenis	Semi-supervised deep learning for pixel-level classification	Semi-supervised deep learning
Sumber perintis	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.
Alias	Semi-SSL segmentation, pseudo-label segmentation, consistency regularization segmentation, label-efficient semantic segmentation	SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN
Berkaitan	5	5
Ringkasan	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.