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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 181–190 of 190 papers

TitleStatusHype
Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-TrainingCode0
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation—0
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation—0
Adversarial Learning for Semi-Supervised Semantic SegmentationCode1
Transferable Semi-supervised Semantic Segmentation—0
Semi Supervised Semantic Segmentation Using Generative Adversarial Network—0
Improved Training for Self-Training by Confidence Assessments—0
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultsCode1
Decoupled Deep Neural Network for Semi-supervised Semantic SegmentationCode0
Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image SegmentationCode0
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