Revisiting Consistency for Semi-Supervised Semantic Segmentation

Revisiting Consistency for Semi-Supervised Semantic Segmentation

Semi-supervised learning is an attractive technique in practical deployments of deep models since it relaxes the dependence on labeled data. It is especially important in the scope of dense prediction because pixel-level annotation requires substantial effort. This paper considers semi-supervised al...

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Bibliographic Details
Main Authors: Ivan Grubišić, Marin Oršić, Siniša Šegvić
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Sensors
Subjects:
semi-supervised learning
semantic segmentation
dense prediction
one-way consistency
deep learning
scene understanding
Online Access:https://www.mdpi.com/1424-8220/23/2/940

Internet

https://www.mdpi.com/1424-8220/23/2/940

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