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Active learning typically focuses on training a model on few labeled examples alone, while unlabeled ones are only used for acquisition.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
Earlier work this paper cites.
Employing em in pool-based active learning for text classification, 1998
M McCallum and K Nigam · 1998
Earlier work this paper cites.
Active+ semi-supervised learning = robust multi-view learning
Ion Muslea, Steven Minton, and Craig A Knoblock · 2002
Earlier work this paper cites.
Learning from labeled and unlabeled data with label propagation
Xiaojin Zhu and Zoubin Ghahramani · 2002
Earlier work this paper cites.
Learning with local and global consistency
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Schölkopf · 2003
Earlier work this paper cites.
Ranking on data manifolds
Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, and Bernhard SchÖlkopf · 2003
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Combining active learning and semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, John Lafferty, and Zoubin Ghahramani · 2003
Earlier work this paper cites.
Exploiting unlabeled data in content-based image retrieval
Zhi-Hua Zhou, Ke-Jia Chen, and Yuan Jiang · 2004
Earlier work this paper cites.
Semi-Supervised Learning
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien · 2006
Earlier work this paper cites.
K-means++: the advantages of careful seeding
D. Arthur and S. Vassilvitskii · 2007
Earlier work this paper cites.
Graph-based active learning based on label propagation
Jun Long, Jianping Yin, Wentao Zhao, and En Zhu · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Active learning literature survey
Burr Settles · 2009
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Ng · 2011
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A. Efros · 2015
Cited alongside, same era.
Unsupervised learning of visual representations using videos
Xiaolong Wang and Abhinav Gupta · 2015
Cited alongside, same era.
Weixin Yang, Lianwen Jin, Dacheng Tao, Zecheng Xie, and Ziyong Feng · 2015
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
Cited alongside, same era.
Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
Cited alongside, same era.
Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
Later among the works it cites.
Revisiting pre-training: An efficient training method for image classification
Bowen Cheng, Yunchao Wei, Honghui Shi, Shiyu Chang, Jinjun Xiong, and Thomas S. Huang · 2018
Later among the works it cites.
Adversarial active learning for deep networks: a margin based approach
Melanie Ducoffe and Frederic Precioso · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Discriminative active learning
Daniel Gissin and Shai Shalev-Shwartz · 2018
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Unsupervised learning by predicting noise
Piotr Bojanowski and Armand Joulin · 2017
Cited alongside, same era.
Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
Cited alongside, same era.
Deep active learning over the long tail
Yonatan Geifman and Ran El-Yaniv · 2017
Cited alongside, same era.
Biased importance sampling for deep neural network training
Angelos Katharopoulos and François Fleuret · 2017
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
Cited alongside, same era.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
Cited alongside, same era.
Adversarial sampling for active learning
Christoph Mayer and Radu Timofte · 2018
Later among the works it cites.
Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
Later among the works it cites.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
Closest in time.
Large-scale visual active learning with deep probabilistic ensembles
Kashyap Chitta, Jose M Alvarez, and Adam Lesnikowski · 2019
Closest in time.
Label propagation for deep semi-supervised learning
A. Iscen, G. Tolias, Y. Avrithis, and O. Chum · 2019
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Ascent: Active supervision for semi-supervised learning
Yanchao Li, Yong li Wang, Dong-Jun Yu, Ye Ning, Peng Hu, and Ruxin Zhao · 2019
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Semi-supervised learning with scarce annotations
Sylvestre-Alvise Rebuffi, Sebastien Ehrhardt, Kai Han, Andrea Vedaldi, and Andrew Zisserman · 2019
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Variational adversarial active learning
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell · 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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