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The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance.
The well-calibrated bayesian
A. P. Dawid · 1982
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The comparison and evaluation of forecasters
Morris H. Degroot and Stephen E. Fienberg · 1983
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Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky · 1995
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Learning by transduction
A. Gammerman, V. Vovk, and V. Vapnik · 1998
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Transductive inference for text classification using support vector machines
Thorsten Joachims · 1999
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Laplacian eigenmaps and spectral techniques for embedding and clustering
Mikhail Belkin and Partha Niyogi · 2002
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2002
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Transductive learning via spectral graph partitioning
Thorsten Joachims · 2003
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Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty · 2003
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Semi-supervised classification by low density separation
Olivier Chapelle and Alexander Zien · 2005
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Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
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Semi-supervised learning literature survey
Xiaojin Jerry Zhu · 2005
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11 label propagation and quadratic criterion
Yoshua Bengio, Olivier Delalleau, and Nicolas Le Roux · 2006
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Principled hybrids of generative and discriminative models
Julia A Lasserre, Christopher M Bishop, and Thomas P Minka · 2006
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Effective self-training for parsing
David McClosky, Eugene Charniak, and Mark Johnson · 2006
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The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Semi-Supervised Learning
Olivier Chapelle, Bernhard Schlkopf, and Alexander Zien · 2010
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Practical variational inference for neural networks
Alex Graves · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Regularization of neural networks using dropconnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus · 2013
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Semi-supervised learning with deep generative models
Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Efficient object localization using convolutional networks
Jonathan Tompson, Ross Goroshin, Arjun Jain, Yann LeCun, and Christoph Bregler · 2015
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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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Structured and efficient variational deep learning with matrix gaussian posteriors
Christos Louizos and Max Welling · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Variational autoencoder for deep learning of images, labels and captions
Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, and Lawrence Carin · 2016
Randaugment: Practical data augmentation with no separate search
Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le · 2019
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Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondřej Chum · 2019
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Dual student: Breaking the limits of the teacher in semi-supervised learning
Zhanghan Ke, Daoye Wang, Qiong Yan, Jimmy Ren, and Rynson W.H. Lau · 2019
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Nlnl: Negative learning for noisy labels
Youngdong Kim, Junho Yim, Juseung Yun, and Junmo Kim · 2019
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Deep metric transfer for label propagation with limited annotated data
Bin Liu, Zhirong Wu, Han Hu, and Stephen Lin · 2019
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A simple baseline for bayesian uncertainty in deep learning
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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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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Xavier Gastaldi · 2017
Cited alongside, same era.
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson · 2019
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On mixup training: Improved calibration and predictive uncertainty for deep neural networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff A Bilmes, Tanmoy Bhattacharya, and Sarah Michalak · 2019
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Interpolation consistency training for semi-supervised learning
Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, and David Lopez-Paz · 2019
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A baseline for multi-label image classification using an ensemble of deep convolutional neural networks
Qian Wang, Ning Jia, and Toby P Breckon · 2019
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Eduard Hovy, Minh-Thang Luong, and Quoc V Le · 2019
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Probabilistic end-to-end noise correction for learning with noisy labels
Kun Yi and Jianxin Wu · 2019
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Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation
Lequan Yu, Shujun Wang, Xiaomeng Li, Chi-Wing Fu, and Pheng-Ann Heng · 2019
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S4l: Self-supervised semi-supervised learning
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer · 2019
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Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring
David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Iterative label improvement: Robust training by confidence based filtering and dataset partitioning
Christian Haase-Schutz, Rainer Stal, H. Hertlein, and B. Sick · 2020
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Videossl: Semi-supervised learning for video classification
Longlong Jing, Toufiq Parag, Zhe Wu, Yingli Tian, and Hongcheng Wang · 2020
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Uncertainty-aware self-training for few-shot text classification
Subhabrata Mukherjee and Ahmed Awadallah · 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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Repetitive reprediction deep decipher for semi-supervised learning
Guo-Hua Wang and Jianxin Wu · 2020
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3d semi-supervised learning with uncertainty-aware multi-view co-training
Yingda Xia, Fengze Liu, Dong Yang, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, and Holger Roth · 2020
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Distance-based learning from errors for confidence calibration
Chen Xing, Sercan Arik, Zizhao Zhang, and Tomas Pfister · 2020
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Zhedong Zheng and Yi Yang · 2020
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Time-consistent self-supervision for semi-supervised learning
Tianyi Zhou, Shengjie Wang, and Jeff A Bilmes · 2020
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