Vertaile menetelmiä

	Puoliohjattu semanttinen segmentointi	Puolivalvottu konvoluutioneuroverkko
Tieteenala	Syväoppiminen	Syväoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2018–2020	2013–2017
Kehittäjä	Multiple (Ouali et al., Zou et al., Chen et al.)	Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)
Tyyppi	Semi-supervised deep learning for pixel-level classification	Semi-supervised deep learning
Alkuperäislähde	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.
Rinnakkaisnimet	Semi-SSL segmentation, pseudo-label segmentation, consistency regularization segmentation, label-efficient semantic segmentation	SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN
Liittyvät	5	5
Tiivistelmä	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.