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This paper presents a simple but performant semi-supervised semantic segmentation approach, called CorrMatch.
Acceleration of stochastic approximation by averaging
Boris T Polyak and Anatoli B Juditsky · 1992
Earlier work this paper cites.
Semi-supervised support vector machines
Kristin Bennett and Ayhan Demiriz · 1998
Earlier work this paper cites.
Transductive inference for text classification using support vector machines
Thorsten Joachims et al · 1999
Earlier work this paper cites.
Learning with labeled and unlabeled data, 2000
Matthias Seeger · 2000
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
Earlier work this paper cites.
Semi-supervised learning literature survey, 2005
Xiaojin Jerry Zhu · 2005
Earlier work this paper cites.
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani · 2006
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2009
Earlier work this paper cites.
Semantic contours from inverse detectors
Bharath Hariharan, Pablo Arbeláez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik · 2011
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
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Earlier work this paper cites.
Learning with pseudo-ensembles
Philip Bachman, Ouais Alsharif, and Doina Precup · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Decoupled deep neural network for semi-supervised semantic segmentation
Seunghoon Hong, Hyeonwoo Noh, and Bohyung Han · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
Earlier work this paper cites.
Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Earlier work this paper cites.
Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
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David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
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Jyh-Jing Hwang, Stella X Yu, Jianbo Shi, Maxwell D Collins, Tien-Ju Yang, Xiao Zhang, and Liang-Chieh Chen · 2019
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