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We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant.
Probability of error of some adaptive pattern-recognition machines
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Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky · 1995
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Automatically generating extraction patterns from untagged text
Ellen Riloff · 1996
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Combining labeled and unlabeled data with co-training
Avrim Blum and Tom Mitchell · 1998
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Learning extraction patterns for subjective expressions
Ellen Riloff and Janyce Wiebe · 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 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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Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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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 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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Some improvements on deep convolutional neural network based image classification
Andrew G Howard · 2013
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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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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 2014
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Learning with pseudo-ensembles
Philip Bachman, Ouais Alsharif, and Doina Precup · 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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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Bayesian dark knowledge
Anoop Korattikara Balan, Vivek Rathod, Kevin P Murphy, and Max Welling · 2015
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 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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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Semi-supervised learning for neural machine translation
Yong Cheng, Wei Xu, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu · 2016
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Dual learning for machine translation
Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma · 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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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Auxiliary deep generative models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Winther · 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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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Yfcc100m: The new data in multimedia research
Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li · 2016
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Good semi-supervised learning that requires a bad gan
Zihang Dai, Zhilin Yang, Fan Yang, William W Cohen, and Ruslan R Salakhutdinov · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
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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Batch normalization is a cause of adversarial vulnerability
Angus Galloway, Anna Golubeva, Thomas Tanay, Medhat Moussa, and Graham W Taylor · 2019
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 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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Learning from noisy large-scale datasets with minimal supervision
Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, and Serge Belongie · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Semi-supervised qa with generative domain-adaptive nets
Zhilin Yang, Junjie Hu, Ruslan Salakhutdinov, and William W Cohen · 2017
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Polynet: A pursuit of structural diversity in very deep networks
Xingcheng Zhang, Zhizhong Li, Chen Change Loy, and Dahua Lin · 2017
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There are many consistent explanations of unlabeled data: Why you should average
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson · 2018
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Using videos to evaluate image model robustness
Keren Gu, Brandon Yang, Jiquan Ngiam, Quoc Le, and Jonathan Shlens · 2019
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Revisiting self-training for neural sequence generation
Junxian He, Jiatao Gu, Jiajun Shen, and Marc’Aurelio Ranzato · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas G Dietterich · 2019
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Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2019
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GPipe: Efficient training of giant neural networks using pipeline parallelism
Yanping Huang, Yonglong Cheng, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V Le, and Zhifeng Chen · 2019
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Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum · 2019
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Leveraging just a few keywords for fine-grained aspect detection through weakly supervised co-training
Giannis Karamanolakis, Daniel Hsu, and Luis Gravano · 2019
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Large scale learning of general visual representations for transfer
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2019
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Do better imagenet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V Le · 2019
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Bridging the domain gap in cross-lingual document classification
Guokun Lai, Barlas Oguz, and Veselin Stoyanov · 2019
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Certainty-driven consistency loss for semi-supervised learning
Yingting Li, Lu Liu, and Robby T Tan · 2019
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Improving robustness without sacrificing accuracy with patch gaussian augmentation
Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, and Ekin D Cubuk · 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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Robustness properties of facebook’s resnext wsl models
A Emin Orhan · 2019
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Lessons from building acoustic models with a million hours of speech
Sree Hari Krishnan Parthasarathi and Nikko Strom · 2019
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
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Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Automatic adaptation of object detectors to new domains using self-training
Aruni Roy Chowdhury, Prithvijit Chakrabarty, Ashish Singh, SouYoung Jin, Huaizu Jiang, Liangliang Cao, and Erik G. Learned-Miller · 2019
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First-order adversarial vulnerability of neural networks and input dimension
Carl-Johann Simon-Gabriel, Yann Ollivier, Leon Bottou, Bernhard Schölkopf, and David Lopez-Paz · 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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Learning to self-train for semi-supervised few-shot classification
Qianru Sun, Xinzhe Li, Yaoyao Liu, Shibao Zheng, Tat-Seng Chua, and Bernt Schiele · 2019
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EfficientNet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
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Fixing the train-test resolution discrepancy
Hugo Touvron, Andrea Vedaldi, Matthijs Douze, and Hervé Jégou · 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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Exploiting monolingual data at scale for neural machine translation
Lijun Wu, Yiren Wang, Yingce Xia, QIN Tao, Jianhuang Lai, and Tie-Yan Liu · 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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Billion-scale semi-supervised learning for image classification
I. Zeki Yalniz, Herv’e J’egou, Kan Chen, Manohar Paluri, and Dhruv Mahajan · 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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Making convolutional networks shift-invariant again
Richard Zhang · 2019
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