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Generative models have the ability to synthesize data points drawn from the data distribution, however, not all generated samples are high quality.
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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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Sergey Zagoruyko and Nikos Komodakis · 2016
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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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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 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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Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2019
Cited alongside, same era.
Coresets for data-efficient training of machine learning models
Baharan Mirzasoleiman, Jeff Bilmes, and Jure Leskovec · 2019
Learning multi-layer latent variable model via variational optimization of short run mcmc for approximate inference
Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, and Ying Nian Wu · 2020
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Learning latent space energy-based prior model
Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, and Ying Nian Wu · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Colin Raffel · 2020
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Adaptive second order coresets for data-efficient machine learning
Omead Pooladzandi, David Davini, and Baharan Mirzasoleiman · 2022
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Cited alongside, same era.