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Active learning aims to develop label-efficient algorithms by querying the most informative samples to be labeled by an oracle.
A mathematical theory of communication
Claude Elwood Shannon · 1948
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Elementary applied statistics: for students in behavioral science
Linton C Freeman · 1965
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Information-based objective functions for active data selection
David JC MacKay · 1992
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Selective sampling using the query by committee algorithm
Yoav Freund, H Sebastian Seung, Eli Shamir, and Naftali Tishby · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
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Employing em and pool-based active learning for text classification
Andrew Kachites McCallumzy and Kamal Nigamy · 1998
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Support vector machine active learning with applications to text classification
Simon Tong and Daphne Koller · 2001
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Incorporating diversity in active learning with support vector machines
Klaus Brinker · 2003
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Active learning with gaussian processes for object categorization
Ashish Kapoor, Kristen Grauman, Raquel Urtasun, and Trevor Darrell · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Multi-class active learning for image classification
Ajay J Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Active learning literature survey
Burr Settles · 2009
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Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel · 2011
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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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Alex Kendall, Vijay Badrinarayanan, and Roberto Cipolla · 2015
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Generative adversarial active learning
Jia-Jie Zhu and José Bento · 2017
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Large-scale celebfaces attributes (celeba) dataset
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2018
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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal · 2019
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Large scale adversarial representation learning
Jeff Donahue and Karen Simonyan · 2019
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Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2016
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Combining satellite imagery and machine learning to predict poverty
Neal Jean, Marshall Burke, Michael Xie, W Matthew Davis, David B Lobell, and Stefano Ermon · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Cost-effective active learning for deep image classification
Keze Wang, Dongyu Zhang, Ya Li, Ruimao Zhang, and Liang Lin · 2016
Cited alongside, same era.
Gradient-free policy architecture search and adaptation
Sayna Ebrahimi, Anna Rohrbach, and Trevor Darrell · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
Cited alongside, same era.
Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2017
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Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning, 2019
Andreas Kirsch, Joost van Amersfoort, and Yarin Gal · 2019
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High-fidelity image generation with fewer labels
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, and Sylvain Gelly · 2019
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Bayesian batch active learning as sparse subset approximation
Robert Pinsler, Jonathan Gordon, Eric Nalisnick, and José Miguel Hernández-Lobato · 2019
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Rethinking deep active learning: Using unlabeled data at model training
Oriane Siméoni, Mateusz Budnik, Yannis Avrithis, and Guillaume Gravier · 2019
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Variational adversarial active learning
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell · 2019
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Bayesian generative active deep learning
Toan Tran, Thanh-Toan Do, Ian Reid, and Gustavo Carneiro · 2019
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A computer vision system for deep learning-based detection of patient mobilization activities in the icu
Serena Yeung, Francesca Rinaldo, Jeffrey Jopling, Bingbin Liu, Rishab Mehra, N Lance Downing, Michelle Guo, Gabriel M Bianconi, Alexandre Alahi, Julia Lee, et al · 2019
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