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Selective classification techniques (also known as reject option) have not yet been considered in the context of deep neural networks (DNNs).
An optimum character recognition system using decision functions
Chao K Chow · 1957
Earlier work this paper cites.
The nearest neighbor classification rule with a reject option
Martin E Hellman · 1970
Earlier work this paper cites.
Distribution-free performance bounds with the resubstitution error estimate
O. Gascuel and G. Caraux · 1992
Earlier work this paper cites.
A method for improving classification reliability of multilayer perceptrons
Luigi Pietro Cordella, Claudio De Stefano, Francesco Tortorella, and Mario Vento · 1995
Earlier work this paper cites.
To reject or not to reject: that is the question-an answer in case of neural classifiers
Claudio De Stefano, Carlo Sansone, and Mario Vento · 2000
Earlier work this paper cites.
Support vector machines with embedded reject option
Giorgio Fumera and Fabio Roli · 2002
Earlier work this paper cites.
Generalization bounds for averaged classifiers
Yoav Freund, Yishay Mansour, and Robert E Schapire · 2004
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Cited alongside, same era.
On the foundations of noise-free selective classification
R. El-Yaniv and Y. Wiener · 2010
Cited alongside, same era.
A risk bound for ensemble classification with a reject option
Kush R Varshney · 2011
Cited alongside, same era.
Active learning via perfect selective classification
Ran El-Yaniv and Yair Wiener · 2012
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
Very deep convolutional neural network based image classification using small training sample size
Shuying Liu and Weihong Deng · 2015
ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Later among the works it cites.
Boosting with abstention
Corinna Cortes, Giulia DeSalvo, and Mehryar Mohri · 2016
Later among the works it cites.
Dropout as a bayesian approximation: representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Later among the works it cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Later among the works it cites.
Sergey Zagoruyko and Nikos Komodakis · 2016
Later among the works it cites.
The Relationship Between Agnostic Selective Classification and Active
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Cited alongside, same era.
R. Gelbhart and R. El-Yaniv · 2017
Closest in time.