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Many practical perception systems exist within larger processes that include interactions with users or additional components capable of evaluating the quality of predicted solutions.
Error correlation and error reduction in ensemble classifiers
K. Tumer and J. Ghosh · 1996
Earlier work this paper cites.
Ensemble learning via negative correlation
Y. Liu and X. Yao · 1999
Earlier work this paper cites.
Cluster ensembles—a knowledge reuse framework for combining multiple partitions
A. Strehl and J. Ghosh · 2003
Earlier work this paper cites.
Creating diversity in ensembles using artificial data
P. Melville and R. J. Mooney · 2005
Earlier work this paper cites.
Learning multiple layers of features from tiny images, 2009
A. Krizhevsky · 2009
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2011 (VOC2011) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2011
Earlier work this paper cites.
Semantic contours from inverse detectors
B. Hariharan, P. Arbelaez, L. Bourdev, S. Maji, and J. Malik · 2011
Earlier work this paper cites.
N-best maximal decoders for part models
D. Park and D. Ramanan · 2011
Earlier work this paper cites.
Diverse M-Best Solutions in Markov Random Fields
D. Batra, P. Yadollahpour, A. Guzman-Rivera, and G. Shakhnarovich · 2012
Earlier work this paper cites.
Multiple Choice Learning: Learning to Produce Multiple Structured Outputs
A. Guzman-Rivera, D. Batra, and P. Kohli · 2012
Earlier work this paper cites.
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O. Russakovsky, J. Deng, J. Krause, A. Berg, and L. Fei-Fei · 2012
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Vision meets Robotics: The KITTI Dataset
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Caffe: An open source convolutional architecture for fast feature embedding
Y. Jia · 2013
Cited alongside, same era.
Return of the devil in the details: Delving deep into convolutional nets
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Cited alongside, same era.
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Cited alongside, same era.
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Deep visual-semantic alignments for generating image descriptions
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Later among the works it cites.
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A. Kirillov, B. Savchynskyy, D. Schlesinger, D. Vetrov, and C. Rother · 2015
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M-best-diverse labelings for submodular energies and beyond
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Cider: Consensus-based image description evaluation
R. Vedantam, C. Lawrence Zitnick, and D. Parikh · 2015
Later among the works it cites.
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G. E. Hinton, O. Vinyals, and J. Dean · 2014
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Microsoft COCO: Common objects in context, 2014
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Cited alongside, same era.
Submodular meets structured: Finding diverse subsets in exponentially-large structured item sets
A. Prasad, S. Jegelka, and D. Batra · 2014
Cited alongside, same era.
Predicting multiple structured visual interpretations
D. Dey, V. Ramakrishna, M. Hebert, and J. Andrew Bagnell · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
WIRED · 2015
Later among the works it cites.
http://caffe.berkeleyvision.org/gathered/examples/cifar10.html, 2016
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