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Self-training via pseudo labeling is a conventional, simple, and popular pipeline to leverage unlabeled data.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Semantic contours from inverse detectors
Bharath Hariharan, Pablo Arbeláez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik · 2011
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
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Learning with pseudo-ensembles
Philip Bachman, Ouais Alsharif, and Doina Precup · 2014
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Generative adversarial networks
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 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
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The pascal visual object classes challenge: A retrospective
Mark Everingham, SM Ali Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2015
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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
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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Not all pixels are equal: Difficulty-aware semantic segmentation via deep layer cascade
Xiaoxiao Li, Ziwei Liu, Ping Luo, Chen Change Loy, and Xiaoou Tang · 2017
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Semi supervised semantic segmentation using generative adversarial network
Nasim Souly, Concetto Spampinato, and Mubarak Shah · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks
Murat Seckin Ayhan and Philipp Berens · 2018
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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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Adversarial learning for semi-supervised semantic segmentation
Wei-Chih Hung, Yi-Hsuan Tsai, Yan-Ting Liou, Yen-Yu Lin, and Ming-Hsuan Yang · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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Data distillation: Towards omni-supervised learning
Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, and Kaiming He · 2018
Cited alongside, same era.
Semi-supervised semantic segmentation with cross-consistency training
Yassine Ouali, Céline Hudelot, and Myriam Tami · 2020
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Not all unlabeled data are equal: learning to weight data in semi-supervised learning
Zhongzheng Ren, Raymond Yeh, and Alexander Schwing · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
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A simple semi-supervised learning framework for object detection
Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li, Han Zhang, Chen-Yu Lee, and Tomas Pfister · 2020
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le · 2020
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Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Yang Zou, Zhiding Yu, BVK Kumar, and Jinsong Wang · 2018
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
Cited alongside, same era.
Consistency-based semi-supervised learning for object detection
Jisoo Jeong, Seungeui Lee, Jeesoo Kim, and Nojun Kwak · 2019
Cited alongside, same era.
Semi-supervised semantic segmentation with high-and low-level consistency
Sudhanshu Mittal, Maxim Tatarchenko, and Thomas Brox · 2019
Cited alongside, same era.
Not all areas are equal: Transfer learning for semantic segmentation via hierarchical region selection
Ruoqi Sun, Xinge Zhu, Chongruo Wu, Chen Huang, Jianping Shi, and Lizhuang Ma · 2019
Cited alongside, same era.
Billion-scale semi-supervised learning for image classification
I Zeki Yalniz, Hervé Jégou, Kan Chen, Manohar Paluri, and Dhruv Mahajan · 2019
Cited alongside, same era.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Cited alongside, same era.
Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
Later among the works it cites.
Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D Cubuk, and Quoc V Le · 2020
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Semi-supervised semantic segmentation with pixel-level contrastive learning from a class-wise memory bank
Inigo Alonso, Alberto Sabater, David Ferstl, Luis Montesano, and Ana C Murillo · 2021
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Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning
Paola Cascante-Bonilla, Fuwen Tan, Yanjun Qi, and Vicente Ordonez · 2021
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Semi-supervised semantic segmentation with cross pseudo supervision
Xiaokang Chen, Yuhui Yuan, Gang Zeng, and Jingdong Wang · 2021
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Re-distributing biased pseudo labels for semi-supervised semantic segmentation: A baseline investigation
Ruifei He, Jihan Yang, and Xiaojuan Qi · 2021
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Semi-supervised semantic segmentation via adaptive equalization learning
Hanzhe Hu, Fangyun Wei, Han Hu, Qiwei Ye, Jinshi Cui, and Liwei Wang · 2021
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Semi-supervised semantic segmentation with directional context-aware consistency
Xin Lai, Zhuotao Tian, Li Jiang, Shu Liu, Hengshuang Zhao, Liwei Wang, and Jiaya Jia · 2021
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R-drop: Regularized dropout for neural networks
Xiaobo Liang, Lijun Wu, Juntao Li, Yue Wang, Qi Meng, Tao Qin, Wei Chen, Min Zhang, and Tie-Yan Liu · 2021
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Classmix: Segmentation-based data augmentation for semi-supervised learning
Viktor Olsson, Wilhelm Tranheden, Juliano Pinto, and Lennart Svensson · 2021
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Meta pseudo labels
Hieu Pham, Zihang Dai, Qizhe Xie, and Quoc V Le · 2021
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A simple baseline for semi-supervised semantic segmentation with strong data augmentation
Jianlong Yuan, Yifan Liu, Chunhua Shen, Zhibin Wang, and Hao Li · 2021
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Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling
Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, and Takahiro Shinozaki · 2021
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Robust mutual learning for semi-supervised semantic segmentation
Pan Zhang, Bo Zhang, Ting Zhang, Dong Chen, and Fang Wen · 2021
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Pixel contrastive-consistent semi-supervised semantic segmentation
Yuanyi Zhong, Bodi Yuan, Hong Wu, Zhiqiang Yuan, Jian Peng, and Yu-Xiong Wang · 2021
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C3-semiseg: Contrastive semi-supervised segmentation via cross-set learning and dynamic class-balancing
Yanning Zhou, Hang Xu, Wei Zhang, Bin Gao, and Pheng-Ann Heng · 2021
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Pseudoseg: Designing pseudo labels for semantic segmentation
Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, and Tomas Pfister · 2021
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