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Active learning aims to reduce the high labeling cost involved in training machine learning models on large datasets by efficiently labeling only the most informative samples.
A mathematical theory of communication
Claude Elwood Shannon · 1948
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Elementary applied statistics: For students in behavioral science
Elmer H. Johnson · 1966
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Query by committee
H. S. Seung, M. Opper, and H. Sompolinsky · 1992
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Gool, Christopher K. Williams, John Winn, and Andrew Zisserman · 2010
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Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature
David H. Douglas and Thomas K. Peucker · 2011
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Visual precis generation using coresets
R. Paul, D. Feldman, D. Rus, and P. Newman · 2014
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Bayesian convolutional neural networks with Bernoulli approximate variational inference
Yarin Gal and Zoubin Ghahramani · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Active image segmentation propagation
S. D. Jain and K. Grauman · 2016
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Deep exploration via bootstrapped dqn
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Alex Kendall and Yarin Gal · 2017
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Active learning for human pose estimation
B. Liu and V. Ferrari · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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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
There are many consistent explanations of unlabeled data: Why you should average
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson · 2019
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
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Autoaugment: Learning augmentation strategies from data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le · 2019
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Consistency regularization and cutmix for semi-supervised semantic segmentation
Geoffrey French, Timo Aila, Samuli Laine, Michal Mackiewicz, and Graham D. Finlayson · 2019
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Universal semi-supervised semantic segmentation
Tarun Kalluri, Girish Varma, Manmohan Chandraker, and C.V. Jawahar · 2019
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Semi-supervised semantic segmentation with high- and low-level consistency
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Cost-effective active learning for deep image classification
Keze Wang, Dongyu Zhang, Ya Li, Ruimao Zhang, and Liang Lin · 2017
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Suggestive annotation: A deep active learning framework for biomedical image segmentation
Lin Yang, Yizhe Zhang, Jianxu Chen, Siyuan Zhang, and Danny Z. Chen · 2017
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The power of ensembles for active learning in image classification
William H. Beluch, Tim Genewein, Andreas Nürnberger, and Jan M. Köhler · 2018
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille · 2018
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Adversarial learning for semi-supervised semantic segmentation
W.-C. Hung, Y.-H. Tsai, Y.-T. Liou, Y.-Y. Lin, and M.-H. Yang · 2018
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Cereals - cost-effective region-based active learning for semantic segmentation
Radek Mackowiak, Philip Lenz, Omair Ghori, Ferran Diego, Oliver Lange, and Carsten Rother · 2018
Cited alongside, same era.
Sudhanshu Mittal, Maxim Tatarchenko, and Thomas Brox · 2019
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Virtual adversarial training: A regularization method for supervised and semi-supervised learning
T. Miyato, S. Maeda, M. Koyama, and S. Ishii · 2019
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Uncertainty-driven semantic segmentation through human-machine collaborative learning
Mahdyar Ravanbakhsh, Tassilo Klein, Kayhan Batmanghelich, and Moin Nabi · 2019
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Variational adversarial active learning
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell · 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 loss for active learning
Donggeun Yoo and In So Kweon · 2019
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S4l: Self-supervised semi-supervised learning
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer · 2019
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Consistency-based semi-supervised active learning: Towards minimizing labeling budget
Anonymous · 2020
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Rethinking deep active learning: Using unlabeled data at model training
Anonymous · 2020
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Semi-supervised semantic segmentation needs strong, high-dimensional perturbations
Anonymous · 2020
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