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Reaching the performance of fully supervised learning with unlabeled data and only labeling one sample per class might be ideal for deep learning applications.
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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Cost-sensitive boosting for classification of imbalanced data
Yanmin Sun, Mohamed S Kamel, Andrew KC Wong, and Yang Wang · 2007
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Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Introduction to semi-supervised learning
Xiaojin Zhu and Andrew B Goldberg · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Multiclass imbalance problems: Analysis and potential solutions
Shuo Wang and Xin Yao · 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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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
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Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study
Isaac Triguero, Salvador García, and Francisco Herrera · 2015
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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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Sergey Zagoruyko and Nikos Komodakis · 2016
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Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Samuli Laine and Timo Aila · 2017
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Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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David Berthelot, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel · 2019
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
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Randaugment: Practical data augmentation with no separate search
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2019
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Survey on deep learning with class imbalance
Justin M Johnson and Taghi M Khoshgoftaar · 2019
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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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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2018
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Kyle Hsu, Sergey Levine, and Chelsea Finn · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Shin Ishii, and Masanori Koyama · 2018
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Image classification with deep learning in the presence of noisy labels: A survey
Görkem Algan and Ilkay Ulusoy · 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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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le · 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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S4l: Self-supervised semi-supervised learning
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer · 2019
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Empirical perspectives on one-shot semi-supervised learning
Leslie N Smith and Adam Conovaloff · 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 survey on semi-supervised learning
Jesper E Van Engelen and Holger H Hoos · 2020
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