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Recently proposed consistency-based Semi-Supervised Learning (SSL) methods such as the $\Pi$-model, temporal ensembling, the mean teacher, or the virtual adversarial training, have advanced the state of the art in several SSL tasks.
Randaugment: Practical data augmentation with no separate search
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 1909
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Herbert Robbins and Sutton Monro · 1951
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Boris T Polyak and Anatoli B Juditsky · 1992
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Training with noise is equivalent to tikhonov regularization
Chris M Bishop · 1995
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Incorporating prior information in machine learning by creating virtual examples
Partha Niyogi, Federico Girosi, and Tomaso Poggio · 1998
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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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Learning from labeled and unlabeled data using graph mincuts
Avrim Blum and Shuchi Chawla · 2001
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Incorporating invariances in non-linear support vector machines
Olivier Chapelle and Bernhard Schölkopf · 2002
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Tangent distance kernels for support vector machines
Bernard Haasdonk and Daniel Keysers · 2002
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Learning from labeled and unlabeled data with label propagation
Xiaojin Zhu and Zoubin Ghahramani · 2002
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Lambertian reflectance and linear subspaces
Ronen Basri and David W Jacobs · 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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Algorithms for manifold learning
Lawrence Cayton · 2005
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Semi-supervised learning literature survey
Xiaojin Jerry Zhu · 2005
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Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani · 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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Markov processes: characterization and convergence , volume 282
Stewart N Ethier and Thomas G Kurtz · 2009
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Manifold models for signals and images
Gabriel Peyré · 2009
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Learning invariant representations of molecules for atomization energy prediction
Grégoire Montavon, Katja Hansen, Siamac Fazli, Matthias Rupp, Franziska Biegler, Andreas Ziehe, Alexandre Tkatchenko, Anatole V Lilienfeld, and Klaus-Robert Müller · 2012
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Invariant scattering convolution networks
Joan Bruna and Stéphane Mallat · 2013
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M Saxe, James L McClelland, and Surya Ganguli · 2013
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Learning with pseudo-ensembles
Philip Bachman, Ouais Alsharif, and Doina Precup · 2014
Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
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Drunet: a dilated-residual U-net deep learning network to segment optic nerve head tissues in optical coherence tomography images
Sripad Krishna Devalla, Prajwal K Renukanand, Bharathwaj K Sreedhar, Giridhar Subramanian, Liang Zhang, Shamira Perera, Jean-Martial Mari, Khai Sing Chin, Tin A Tun, Nicholas G Strouthidis, et al · 2018
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Smooth neighbors on teacher graphs for semi-supervised learning
Yucen Luo, Jun Zhu, Mengxi Li, Yong Ren, and Bo Zhang · 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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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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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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Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
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Deep learning and hierarchal generative models
Elchanan Mossel · 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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High-dimensional dynamics of generalization error in neural networks
Madhu S Advani and Andrew M Saxe · 2017
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On the information bottleneck theory of deep learning
Andrew Michael Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan Daniel Tracey, and David Daniel Cox · 2018
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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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Large data and zero noise limits of graph-based semi-supervised learning algorithms
Matthew M Dunlop, Dejan Slepčev, Andrew M Stuart, and Matthew Thorpe · 2019
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Modelling the influence of data structure on learning in neural networks
Sebastian Goldt, Marc Mézard, Florent Krzakala, and Lenka Zdeborová · 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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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le · 2019
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Learning data augmentation strategies for object detection
Barret Zoph, Ekin D Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, and Quoc V Le · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Generalisation error in learning with random features and the hidden manifold model
Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mezard, and Lenka Zdeborová · 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 H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Ilya Kostrikov, Denis Yarats, and Rob Fergus · 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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