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Active learning strategies aim to train high-performance models with minimal labeled data by selecting the most informative instances for labeling.
“Calculation of the wasserstein distance between probability distributions on the line,”
SS Vallender, · 1974
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“Information, prediction, and query by committee,”
Yoav Freund, H Sebastian Seung, Eli Shamir, and Naftali Tishby, · 1992
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“Committee-based sampling for training probabilistic classifiers,”
Ido Dagan and Sean P Engelson, · 1995
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“Long short-term memory,”
Sepp Hochreiter and Jürgen Schmidhuber, · 1997
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“High-dimensional data analysis: The curses and blessings of dimensionality,”
David L Donoho et al., · 2000
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“Balancing exploration and exploitation: A new algorithm for active machine learning,”
Thomas Osugi, Deng Kim, and Stephen Scott, · 2005
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“The pascal visual object classes (voc) challenge,”
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman, · 2010
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“Near-optimal bayesian active learning with noisy observations,”
Daniel Golovin, Andreas Krause, and Debajyoti Ray, · 2010
Earlier work this paper cites.
“Adaptive active learning for image classification,”
Xin Li and Yuhong Guo, · 2013
Earlier work this paper cites.
“Selecting influential examples: Active learning with expected model output changes,”
Alexander Freytag, Erik Rodner, and Joachim Denzler, · 2014
Earlier work this paper cites.
“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
Earlier work this paper cites.
“Adam: A method for stochastic optimization,”
Diederik P. Kingma and Jimmy Ba, · 2015
Earlier work this paper cites.
“Active image segmentation propagation,”
Suyog Dutt Jain and Kristen Grauman, · 2016
Cited alongside, same era.
“Ssd: Single shot multibox detector,”
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg, · 2016
Cited alongside, same era.
“Deep bayesian active learning with image data,”
Yarin Gal, Riashat Islam, and Zoubin Ghahramani, · 2017
Cited alongside, same era.
“Suggestive annotation: A deep active learning framework for biomedical image segmentation,”
Lin Yang, Yizhe Zhang, Jianxu Chen, Siyuan Zhang, and Danny Z Chen, · 2017
Cited alongside, same era.
“Learning algorithms for active learning,”
Philip Bachman, Alessandro Sordoni, and Adam Trischler, · 2017
Cited alongside, same era.
“A meta-learning approach to one-step active-learning,”
Gabriella Contardo, Ludovic Denoyer, and Thierry Artières, · 2017
Cited alongside, same era.
“Learning loss for active learning,”
Donggeun Yoo and In So Kweon, · 2019
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“Variational adversarial active learning,”
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell, · 2019
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“Active learning for deep detection neural networks,”
Hamed H. Aghdam, Abel Gonzalez-Garcia, Antonio M. López, and Joost van de Weijer, · 2019
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“Contextual diversity for active learning,”
Sharat Agarwal, Himanshu Arora, Saket Anand, and Chetan Arora, · 2020
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“Reinforced active learning for image segmentation,”
Arantxa Casanova, Pedro O. Pinheiro, Negar Rostamzadeh, and Christopher J. Pal, · 2020
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“Multiple instance active learning for object detection,”
Tianning Yuan, Fang Wan, Mengying Fu, Jianzhuang Liu, Songcen Xu, Xiangyang Ji, and Qixiang Ye, · 2021
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“Focal loss for dense object detection,”
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár, · 2017
Cited alongside, same era.
“Active learning for convolutional neural networks: A core-set approach,”
Ozan Sener and Silvio Savarese, · 2018
Cited alongside, same era.
“Cost-sensitive active learning for intracranial hemorrhage detection,”
Weicheng Kuo, Christian Häne, Esther L. Yuh, Pratik Mukherjee, and Jitendra Malik, · 2018
Cited alongside, same era.
“Learning how to actively learn: A deep imitation learning approach,”
Ming Liu, Wray Buntine, and Gholamreza Haffari, · 2018
Cited alongside, same era.
“Discovering general-purpose active learning strategies,”
Ksenia Konyushkova, Raphael Sznitman, and Pascal Fua, · 2018
Cited alongside, same era.
“Learning to match anchors for visual object detection,”
Xiaosong Zhang, Fang Wan, Chang Liu, Xiangyang Ji, and Qixiang Ye, · 2021
Later among the works it cites.
“Interpolation-based semi-supervised learning for object detection,”
Jisoo Jeong, Vikas Verma, Minsung Hyun, Juho Kannala, and Nojun Kwak, · 2021
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“Entropy-based active learning for object detection with progressive diversity constraint,”
Jiaxi Wu, Jiaxin Chen, and Di Huang, · 2022
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“Scale-equivalent distillation for semi-supervised object detection,”
Qiushan Guo, Yao Mu, Jianyu Chen, Tianqi Wang, Yizhou Yu, and Ping Luo, · 2022
Later among the works it cites.
“Multiple instance differentiation learning for active object detection,”
Fang Wan, Qixiang Ye, Tianning Yuan, Songcen Xu, Jianzhuang Liu, Xiangyang Ji, and Qingming Huang, · 2023
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