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Active learning algorithms select a subset of data for annotation to maximize the model performance on a budget.
A sequential algorithm for training text classifiers
David D Lewis and William A Gale · 1994
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Estimating the mean and variance of the target probability distribution
David A Nix and Andreas S Weigend · 1994
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Nonlinear dimensionality reduction by locally linear embedding
Sam T Roweis and Lawrence K Saul · 2000
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Stochastic neighbor embedding
Geoffrey Hinton and Sam T Roweis · 2002
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Mit ocw - 18.650:lec31.pdf
Dmitry Panchenko · 2003
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Diverse ensembles for active learning
Prem Melville and Raymond J Mooney · 2004
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Heteroscedastic gaussian process regression
Quoc V Le, Alex J Smola, and Stéphane Canu · 2005
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Multi-class ensemble-based active learning
Christine Körner and Stefan Wrobel · 2006
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An analysis of active learning strategies for sequence labeling tasks
Burr Settles and Mark Craven · 2008
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Multiple-instance active learning
Burr Settles, Mark Craven, and Soumya Ray · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Multi-class active learning for image classification
Ajay J Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos · 2009
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Active learning literature survey
Burr Settles · 2009
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Clustered pose and nonlinear appearance models for human pose estimation
Sam Johnson and Mark Everingham · 2010
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Learning effective human pose estimation from inaccurate annotation
Sam Johnson and Mark Everingham · 2011
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Maximizing expected model change for active learning in regression
W. Cai, Y. Zhang, and J. Zhou · 2013
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A convex optimization framework for active learning
Ehsan Elhamifar, Guillermo Sapiro, Allen Yang, and S Shankar Sasrty · 2013
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2d human pose estimation: New benchmark and state of the art analysis
Mykhaylo Andriluka, Leonid Pishchulin, Peter Gehler, and Bernt Schiele · 2014
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Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Deeppose: Human pose estimation via deep neural networks
A. Toshev and C. Szegedy · 2014
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Accelerating t-sne using tree-based algorithms
Laurens van der Maaten · 2014
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Diverse expected gradient active learning for relative attributes
Xinge You, Ruxin Wang, and Dacheng Tao · 2014
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Efficient per-example gradient computations, 2015
Ian Goodfellow · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Active discriminative text representation learning
Ye Zhang, Matthew Lease, and Byron Wallace · 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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Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning
Stefan Depeweg, Jose Miguel Hernandez-Lobato, Finale Doshi-Velez, and Steffen Udluft · 2018
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Structured aleatoric uncertainty in human pose estimation
Nitesh B Gundavarapu, Divyansh Srivastava, Rahul Mitra, Abhishek Sharma, and Arjun Jain · 2019
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Region-based active learning for efficient labeling in semantic segmentation
Tejaswi Kasarla, Gattigorla Nagendar, Guruprasad M Hegde, Vineeth Balasubramanian, and CV Jawahar · 2019
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Multi-class active learning by uncertainty sampling with diversity maximization
Yi Yang, Zhigang Ma, Feiping Nie, Xiaojun Chang, and Alexander G Hauptmann · 2015
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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 learning for speech recognition: the power of gradients, 2016
Jiaji Huang, Rewon Child, Vinay Rao, Hairong Liu, Sanjeev Satheesh, and Adam Coates · 2016
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Stacked hourglass networks for human pose estimation
Alejandro Newell, Kaiyu Yang, and Jia Deng · 2016
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Batch mode active learning for regression with expected model change
Wenbin Cai, Muhan Zhang, and Ya Zhang · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Deep high-resolution representation learning for human pose estimation
Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang · 2019
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Learning loss for active learning
D. Yoo and I. S. Kweon · 2019
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Gradient-based active learning query strategy for end-to-end speech recognition
Y. Yuan, S. Chung, and H. Kang · 2019
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Toward fast and accurate human pose estimation via soft-gated skip connections
Adrian Bulat, Jean Kossaifi, Georgios Tzimiropoulos, and Maja Pantic · 2020
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Active learning for bayesian 3d hand pose estimation
Razvan Caramalau, Binod Bhattarai, and Tae-Kyun Kim · 2020
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Bacoun: Bayesian classifers with out-of-distribution uncertainty
Théo Guénais, Dimitris Vamvourellis, Yaniv Yacoby, Finale Doshi-Velez, and Weiwei Pan · 2020
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Adversarial sampling for active learning
Christoph Mayer and Radu Timofte · 2020
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LEt-SNE: A hybrid approach to data embedding and visualization of hyperspectral imagery
Megh Shukla, Biplab Banerjee, and Krishna Mohan Buddhiraju · 2020
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Uncertainty estimation using a single deep deterministic neural network
Joost Van Amersfoort, Lewis Smith, Yee Whye Teh, and Yarin Gal · 2020
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A survey of uncertainty in deep neural networks, 2021
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang, Richard Bamler, and Xiao Xiang Zhu · 2021
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A mathematical analysis of learning loss for active learning in regression
Megh Shukla and Shuaib Ahmed · 2021
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