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We propose using active learning based techniques to further improve the state-of-the-art semi-supervised learning MixMatch algorithm.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 1905
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Least squares quantization in pcm
Stuart Lloyd · 1982
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Query by committee
H Sebastian Seung, Manfred Opper, and Haim Sompolinsky · 1992
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Improving generalization with active learning
David Cohn, Les Atlas, and Richard Ladner · 1994
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Heterogeneous uncertainty sampling for supervised learning
David D Lewis and Jason Catlett · 1994
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A sequential algorithm for training text classifiers
David D Lewis and William A Gale · 1994
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Query learning strategies using boosting and bagging
Naoki Abe and Hiroshi Mamitsuka · 1998
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Employing em and pool-based active learning for text classification
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Nicholas Roy and Andrew McCallum · 2001
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Active + semi-supervised learning = robust multi-view learning
Ion Muslea, Steven Minton, and Craig A Knoblock · 2002
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Xiaojin Zhu, John Lafferty, and Zoubin Ghahramani · 2003
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Sanjoy Dasgupta, Daniel J Hsu, and Claire Monteleoni · 2008
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Burr Settles · 2009
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Katrin Tomanek and Udo Hahn · 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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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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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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Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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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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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
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Realistic evaluation of deep semi-supervised learning algorithms
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