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In active learning, the focus is mainly on the selection strategy of unlabeled data for enhancing the generalization capability of the next learning cycle.
Learning theory
M. F. Balcan, A. Broder, and T. Zhang · 2007
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Entropy-based active learning for object recognition
A. Holub, P. Perona, and M.C. Burl · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Active learning literature survey
Burr Settles · 2010
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An analysis of single layer networks in unsupervised feature learning
A. Coates, H. Lee, and A. Y. Ng · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
T. DeVries and G. W. Taylor · 2017
Cited alongside, same era.
Deep bayesian active learning with image data
Y. Gal, R. Islam, and Z. Ghahramani · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, and O. Vinyals B. Recht and · 2017
Cited alongside, same era.
The power of ensembles for active learning in image classification
Active learning for convolutional neural networks: A core-set approach
O. Sener and S. Savarese · 2018
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mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2018
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Data augmentation instead of explicit regularization
A. Hernandez-Garcia and P. Konig · 2019
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Active learning with partial feedback
P. Hu, Z. C. Lipton, A. Anandkumar, and D. Ramanan · 2019
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Rethinking deep active learning: Using unlabeled data at model training
O. Simeoni, M. Budnik, Y. Avrithis, and G. Gravier · 2019
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Learning loss for active learning
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W. H. Beluch, T. Genewein, A. Nurnberger, and J. M. Kohler · 2018
Cited alongside, same era.
Active learning for regression tasks with expected model output changes
C. Kading, E. Rodner, A. Freytag, O. Mothes, B. Barz, and J. Denzler · 2018
Cited alongside, same era.
Generalization in machine learning via analytical learning theory
K. Kawaguchi, Y. Bengio, V. Verma, and L. P. Kaelbling · 2018
Cited alongside, same era.
D. Yoo and I. S. Kweon · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo · 2019
Later among the works it cites.