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When we can not assume a large amount of annotated data , active learning is a good strategy.
A mathematical theory of communication
Claude Elwood Shannon · 1948
Earlier work this paper cites.
Mining the web with active hidden markov models
Tobias Scheffer, Christian Decomain, and Stefan Wrobel · 2001
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Support vector machine active learning for image retrieval
Simon Tong and Edward Chang · 2001
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Incorporating diversity in active learning with support vector machines
Klaus Brinker · 2003
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Reducing labeling effort for structured prediction tasks
Aron Culotta and Andrew McCallum · 2005
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Active learning for class imbalance problem
Seyda Ertekin, Jian Huang, and C Lee Giles · 2007
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Active learning for word sense disambiguation with methods for addressing the class imbalance problem
Jingbo Zhu and Eduard Hovy · 2007
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Hierarchical sampling for active learning
Sanjoy Dasgupta and Daniel J. Hsu · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Recognizing indoor scenes
Ariadna Quattoni and Antonio Torralba · 2009
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Active learning literature survey
Burr Settles · 2010
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Incorporating incremental and active learning for scene classification
Xianglin Li, Runqiu Guo, and Jun Cheng · 2012
Cited alongside, same era.
Class imbalance and active learning
Josh Attenberg and Seyda Ertekin · 2013
Cited alongside, same era.
Food-101 – mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
Cited alongside, same era.
Active batch selection via convex relaxations with guaranteed solution bounds
Shayok Chakraborty, Vineeth Nallure Balasubramanian, Qian Sun, Sethuraman Panchanathan, and Jieping Ye · 2015
Cited alongside, same era.
Active learning by learning
Wei-Ning Hsu and Hsuan-Tien Lin · 2015
Cited alongside, same era.
Submodularity in data subset selection and active learning
Kai Wei, Rishabh Iyer, and Jeff Bilmes · 2015
Cited alongside, same era.
Fine-tuning convolutional neural networks for biomedical image analysis: Actively and incrementally
Zongwei Zhou, Jae Y. Shin, Lei Zhang, Suryakanth R. Gurudu, Michael B. Gotway, and Jianming Liang · 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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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda, Atsuto Maki, and Maciej A. Mazurowski · 2018
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Fei-Fei Li · 2015
Cited alongside, same era.
Local uncertainty sampling for large-scale multi-class logistic regression
Lei Han, Kean Ming Tan, Ting Yang, and Tong Zhang · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
Cited alongside, same era.
Learning active learning from data
Ksenia Konyushkova, Raphael Sznitman, and Pascal Fua · 2017
Cited alongside, same era.
Weight decay scheduling and knowledge distillation for active learning
Juseung Yun, Byungjoo Kim, and Junmo Kim
Cited in the paper.
Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal · 2020
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Consistency-based semi-supervised active learning: Towards minimizing labeling cost
Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan Ö Arık, Larry S Davis, and Tomas Pfister · 2020
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Active learning for imbalanced datasets
Umang Aggarwal, Adrian Popescu, and Celine Hudelot · 2020
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Deep active learning for biased datasets via fisher kernel self-supervision
Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi, and Sotaro Tsukizawa · 2020
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Selection via proxy: Efficient data selection for deep learning
Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, and Matei Zaharia · 2020
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