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In this paper we propose a method to construct a selective classifier given a trained neural network Selective classification for deep neural networks. Our method allows a user to set a desired risk level
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At test time, the classifier rejects instances as needed, to grant the desired risk (with high probability). We consider the problem of selective prediction (also known as reject option) in deep neural networks, and introduce selectivenet, a deep neural architecture with an integrated reject option. At test time, the classifier.
