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Real-world classifiers can benefit from the option of abstaining from predicting on samples where they have low confidence.
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Abhijit Bendale and Terrance E Boult · 2016
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Jakob Nikolas Kather, Cleo-Aron Weis, Francesco Bianconi, Susanne M Melchers, Lothar R Schad, Timo Gaiser, Alexander Marx, and Frank Gerrit Zöllner · 2016
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Enhancing the reliability of out-of-distribution image detection in neural networks
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Selective classification can magnify disparities across groups
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Shiyu Liang, Yixuan Li, and R. Srikant · 2018
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Likelihood Ratios for Out-of-Distribution Detection , pages 14707––14718
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Augmenting softmax information for selective classification with out-of-distribution data
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