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The performance of deep neural networks improves with more annotated data.
Training connectionist networks with queries and selective sampling
L. E. Atlas, D. A. Cohn, and R. E. Ladner · 1990
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Query by committee
H. S. Seung, M. Opper, and H. Sompolinsky · 1992
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Heterogeneous uncertainty sampling for supervised learning
D. D. Lewis and J. Catlett · 1994
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A sequential algorithm for training text classifiers
D. D. Lewis and W. A. Gale · 1994
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Employing em and pool-based active learning for text classification
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Toward optimal active learning through monte carlo estimation of error reduction
N. Roy and A. McCallum · 2001
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Support vector machine active learning with applications to text classification
S. Tong and D. Koller · 2001
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Active learning using pre-clustering
H. T. Nguyen and A. Smeulders · 2004
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Margin-based active learning for structured output spaces
D. Roth and K. Small · 2006
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An analysis of active learning strategies for sequence labeling tasks
B. Settles and M. Craven · 2008
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Multiple-instance active learning
B. Settles, M. Craven, and S. Ray · 2008
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Link-based active learning
M. Bilgic and L. Getoor · 2009
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Statistical methods in practice: for scientists and technologists
R. Boddy and G. Smith · 2009
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Multi-class active learning for image classification
A. JOSHI · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky · 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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Object detection with discriminatively trained part-based models
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan · 2010
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Active instance sampling via matrix partition
Y. Guo · 2010
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Combining generative and discriminative models for semantic segmentation of ct scans via active learning
J. E. Iglesias, E. Konukoglu, A. Montillo, Z. Tu, and A. Criminisi · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Active learning
B. Settles · 2012
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A convex optimization framework for active learning
E. Elhamifar, G. Sapiro, A. Yang, and S. Shankar Sasrty · 2013
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Latent structured active learning
W. Luo, A. Schwing, and R. Urtasun · 2013
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2d human pose estimation: New benchmark and state of the art analysis
M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele · 2014
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Selecting influential examples: Active learning with expected model output changes
A. Freytag, E. Rodner, and J. Denzler · 2014
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Multi-level adaptive active learning for scene classification
X. Li and Y. Guo · 2014
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Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
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Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 2016
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Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. Girshick · 2016
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Deep bayesian active learning with image data
Y. Gal, R. Islam, and Z. Ghahramani · 2017
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Active learning for human pose estimation
B. Liu and V. Ferrari · 2017
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Representation learning by learning to count
M. Noroozi, H. Pirsiavash, and P. Favaro · 2017
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Hierarchical subquery evaluation for active learning on a graph
O. Mac Aodha, N. Campbell, J. Kautz, and G. J. Brostow · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Large-scale live active learning: Training object detectors with crawled data and crowds
S. Vijayanarasimhan and K. Grauman · 2014
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Learning to see by moving
P. Agrawal, J. Carreira, and J. Malik · 2015
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Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
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Context aware active learning of activity recognition models
M. Hasan and A. K. Roy-Chowdhury · 2015
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Non-uniform subset selection for active learning in structured data
S. Paul, J. H. Bappy, and A. K. Roy-Chowdhury · 2017
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Cost-effective active learning for deep image classification
K. Wang, D. Zhang, Y. Li, R. Zhang, and L. Lin · 2017
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Suggestive annotation: A deep active learning framework for biomedical image segmentation
L. Yang, Y. Zhang, J. Chen, S. Zhang, and D. Z. Chen · 2017
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
R. Zhang, P. Isola, and A. A. Efros · 2017
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Fine-tuning convolutional neural networks for biomedical image analysis: Actively and incrementally
Z. Zhou, J. Shin, L. Zhang, S. Gurudu, M. Gotway, and J. Liang · 2017
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The power of ensembles for active learning in image classification
W. H. Beluch, T. Genewein, A. Nürnberger, and J. M. Köhler · 2018
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A robust and effective approach towards accurate metastasis detection and pn-stage classification in breast cancer
B. Lee and K. Paeng · 2018
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Active self-paced learning for cost-effective and progressive face identification
L. Lin, K. Wang, D. Meng, W. Zuo, and L. Zhang · 2018
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Exploring the limits of weakly supervised pretraining
D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten · 2018
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Development and validation of deep learning–based automatic detection algorithm for malignant pulmonary nodules on chest radiographs
J. G. Nam, S. Park, E. J. Hwang, J. H. Lee, K.-N. Jin, K. Y. Lim, T. H. Vu, J. H. Sohn, S. Hwang, J. M. Goo, et al · 2018
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Active learning for convolutional neural networks: A core-set approach
O. Sener and S. Savarese · 2018
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Towards human-machine cooperation: Self-supervised sample mining for object detection
K. Wang, X. Yan, D. Zhang, L. Zhang, and L. Lin · 2018
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