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Semi-Supervised Semantic Segmentation

Models that are trained with a small number of labeled examples and a large number of unlabeled examples and whose aim is to learn to segment an image (i.e. assign a class to every pixel).

Papers

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Showing 171–180 of 190 papers

TitleStatusHype
Structured Consistency Loss for semi-supervised semantic segmentation—0
Discovering Latent Classes for Semi-Supervised Semantic Segmentation—0
Saliency Guided Self-attention Network for Weakly and Semi-supervised Semantic SegmentationCode0
Semi-supervised semantic segmentation needs strong, high-dimensional perturbations—0
Semi-supervised Semantic Segmentation using Auxiliary Network—0
Semi-Supervised Semantic Segmentation with High- and Low-level ConsistencyCode0
Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation—0
Curriculum semi-supervised segmentationCode0
S4-Net: Geometry-Consistent Semi-Supervised Semantic Segmentation—0
Universal Semi-Supervised Semantic SegmentationCode0
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