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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 176–190 of 190 papers

TitleStatusHype
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
Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays—0
Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial LearningCode0
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
Transferable Semi-supervised Semantic Segmentation—0
Semi Supervised Semantic Segmentation Using Generative Adversarial Network—0
Improved Training for Self-Training by Confidence Assessments—0
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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