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Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce.
Transformation invariance in pattern recognition—tangent distance and tangent propagation
Patrice Y Simard, Yann A LeCun, John S Denker, and Bernard Victorri · 1998
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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 learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston · 2008
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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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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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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Learning word vectors for sentiment analysis
Andrew L Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts · 2011
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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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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Deep learning via semi-supervised embedding
Jason Weston, Frédéric Ratle, Hossein Mobahi, and Ronan Collobert · 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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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Learning with pseudo-ensembles
Philip Bachman, Ouais Alsharif, and Doina Precup · 2014
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Deep speech: Scaling up end-to-end speech recognition
Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, et al · 2014
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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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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Semi-supervised sequence learning
Andrew M Dai and Quoc V Le · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Image-based recommendations on styles and substitutes
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel · 2015
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Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
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Auxiliary deep generative models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Winther · 2016
Cited alongside, same era.
Adversarial training methods for semi-supervised text classification
Takeru Miyato, Andrew M Dai, and Ian Goodfellow · 2016
Cited alongside, same era.
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
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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Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin A Raffel, Ekin Dogus Cubuk, and Ian Goodfellow · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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Revisiting lstm networks for semi-supervised text classification via mixed objective function
Devendra Singh Sachan, Manzil Zaheer, and Ruslan Salakhutdinov · 2018
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Cited alongside, same era.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov · 2016
Cited alongside, same era.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
Cited alongside, same era.
Good semi-supervised learning that requires a bad gan
Zihang Dai, Zhilin Yang, Fan Yang, William W Cohen, and Ruslan R Salakhutdinov · 2017
Cited alongside, same era.
Learning discrete representations via information maximizing self-augmented training
Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama · 2017
Cited alongside, same era.
Deep pyramid convolutional neural networks for text categorization
Rie Johnson and Tong Zhang · 2017
Cited alongside, same era.
Invariant representation learning for robust deep networks
Julian Salazar, Davis Liang, Zhiheng Huang, and Zachary C Lipton · 2018
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Switchout: an efficient data augmentation algorithm for neural machine translation
Xinyi Wang, Hieu Pham, Zihang Dai, and Graham Neubig · 2018
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Qanet: Combining local convolution with global self-attention for reading comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V Le · 2018
Later among the works it cites.
There are many consistent explanations of unlabeled data: Why you should average
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson · 2019
Closest in time.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
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Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, and John C Duchi · 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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Data-efficient image recognition with contrastive predictive coding
Olivier J Hénaff, Ali Razavi, Carl Doersch, SM Eslami, and Aaron van den Oord · 2019
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Semi-supervised learning by label gradient alignment
Jacob Jackson and John Schulman · 2019
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Wouter Kool, Herke van Hoof, and Max Welling · 2019
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Robustness to adversarial perturbations in learning from incomplete data
Amir Najafi, Shin-ichi Maeda, Masanori Koyama, and Takeru Miyato · 2019
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Specaugment: A simple data augmentation method for automatic speech recognition
Daniel S Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D Cubuk, and Quoc V Le · 2019
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Mixture models for diverse machine translation: Tricks of the trade
Tianxiao Shen, Myle Ott, Michael Auli, and Marc’Aurelio Ranzato · 2019
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Are labels required for improving adversarial robustness?
Robert Stanforth, Alhussein Fawzi, Pushmeet Kohli, et al · 2019
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Selfie: Self-supervised pretraining for image embedding
Trieu H Trinh, Minh-Thang Luong, and Quoc V Le · 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 embedding learning via invariant and spreading instance feature
Mang Ye, Xu Zhang, Pong C Yuen, and Shih-Fu Chang · 2019
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Adversarially robust generalization just requires more unlabeled data
Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John Hopcroft, and Liwei Wang · 2019
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S 4 l: Self-supervised semi-supervised learning
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer · 2019
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