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Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator.
A mathematical theory of communication
Claude Elwood Shannon · 1948
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Information theory and statistical mechanics. ii
Edwin T Jaynes · 1957
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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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Information theory and statistics
Solomon Kullback · 1997
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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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Minimax entropy and maximum likelihood
Marian Grendár and Marián Grendár · 2000
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Toward optimal active learning through monte carlo estimation of error reduction
Nicholas Roy and Andrew McCallum · 2001
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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 using pre-clustering
Hieu T Nguyen and Arnold Smeulders · 2004
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Query by committee made real
Ran Gilad-Bachrach, Amir Navot, and Naftali Tishby · 2006
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Entropy regularization., 2006
Yves Grandvalet and Yoshua Bengio · 2006
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K-means++: The advantages of careful seeding
David Arthur and Sergei Vassilvitskii · 2007
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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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High-dimensional data analysis
Damien François · 2008
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ImageNet: A Large-Scale Hierarchical Image Database
Jia Deng, Wei Dong, Richard Socher, Lie-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Discriminative clustering by regularized information maximization
Andreas Krause, Pietro Perona, and Ryan Gomes · 2010
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Metric learning for large scale image classification: Generalizing to new classes at near-zero cost
Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka · 2012
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Active learning
Burr Settles · 2012
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Adaptive active learning for image classification
Xin Li and Yuhong Guo · 2013
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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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Active learning literature survey. 2010
Burr Settles · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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A new active labeling method for deep learning
D. Wang and Y. Shang · 2014
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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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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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Cost-sensitive active learning for intracranial hemorrhage detection
Weicheng Kuo, Christian Häne, Esther Yuh, Pratik Mukherjee, and Jitendra Malik · 2018
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Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network
Dwarikanath Mahapatra, Behzad Bozorgtabar, Jean-Philippe Thiran, and Mauricio Reyes · 2018
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Adversarial sampling for active learning
Christoph Mayer and Radu Timofte · 2018
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Cited alongside, same era.
Introducing geometry in active learning for image segmentation
Ksenia Konyushkova, Raphael Sznitman, and Pascal Fua · 2015
Cited alongside, same era.
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
Cited alongside, same era.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
Jost Tobias Springenberg · 2015
Cited alongside, same era.
Simultaneous deep transfer across domains and tasks
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko · 2015
Cited alongside, same era.
Querying discriminative and representative samples for batch mode active learning
Zheng Wang and Jieping Ye · 2015
Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Cited alongside, same era.
Low-shot learning with imprinted weights
Hang Qi, Matthew Brown, and David G Lowe · 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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The inaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2018
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Bdd100k: A diverse driving video database with scalable annotation tooling
Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, and Trevor Darrell · 2018
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
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Computer vision algorithms and hardware implementations: A survey
Xin Feng, Youni Jiang, Xuejiao Yang, Ming Du, and Xin Li · 2019
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Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Andreas Kirsch, Joost van Amersfoort, and Yarin Gal · 2019
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Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2019
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Semi-supervised domain adaptation via minimax entropy
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko · 2019
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Variational adversarial active learning
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell · 2019
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Minimax entropy network: Learning category-invariant features for domain adaptation
Chaofan Tao, Fengmao Lv, Lixin Duan, and Min Wu · 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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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal · 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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On robustness and transferability of convolutional neural networks
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D’Amour, Dan Moldovan, et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Uncertainty-guided continual learning with bayesian neural networks
Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, and Marcus Rohrbach · 2020
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Adversarial continual learning
Sayna Ebrahimi, Franziska Meier, Roberto Calandra, Trevor Darrell, and Marcus Rohrbach · 2020
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Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2020
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