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Often, labeling large amount of data is challenging due to high labeling cost limiting the application domain of deep learning techniques.
Active learning with statistical models
David A Cohn, Zoubin Ghahramani, and Michael I Jordan · 1996
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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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Diverse ensembles for active learning
Prem Melville and Raymond J Mooney · 2004
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Active learning using pre-clustering
Hieu T Nguyen and Arnold Smeulders · 2004
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One-shot learning of object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2006
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 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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The Pascal Visual Object Classes (VOC) Challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
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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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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Hierarchical subquery evaluation for active learning on a graph
Oisin Mac Aodha, Neill DF Campbell, Jan Kautz, and Gabriel J Brostow · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Querying discriminative and representative samples for batch mode active learning
Zheng Wang and Jieping Ye · 2015
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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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Fisher Yu, Vladlen Koltun, and Thomas Funkhouser · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Ranking CGANs: subjective control over semantic image attributes
Yassir Saquil, Kwang In Kim, and Peter Hall · 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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Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2018
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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Gabriele Campanella, Matthew G Hanna, Luke Geneslaw, Allen Miraflor, Vitor Werneck Krauss Silva, Klaus J Busam, Edi Brogi, Victor E Reuter, David S Klimstra, and Thomas J Fuchs · 2019
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YouTube-8M: A large-scale video classification benchmark
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The cityscapes dataset for semantic urban scene understanding
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Deep residual learning for image recognition
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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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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Bayesian batch active learning as sparse subset approximation
Robert Pinsler, Jonathan Gordon, Eric Nalisnick, and José Miguel Hernández-Lobato · 2019
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Variational adversarial active learning
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell · 2019
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Active learning for decision-making from imbalanced observational data
Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, and Samuel Kaski · 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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Learning loss for active learning
Donggeun Yoo and In So Kweon · 2019
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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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State-relabeling adversarial active learning
Beichen Zhang, Liang Li, Shijie Yang, Shuhui Wang, Zheng-Jun Zha, and Qingming Huang · 2020
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