Open AccessProceedings Article
Selective Classification for Deep Neural Networks
Yonatan Geifman,Ran El-Yaniv +1 more
- Vol. 30, pp 4878-4887
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TLDR
A method to construct a selective classifier given a trained neural network, which allows a user to set a desired risk level and the classifier rejects instances as needed, to grant the desired risk (with high probability).Abstract:
Selective classification techniques (also known as reject option) have not yet been considered in the context of deep neural networks (DNNs). These techniques can potentially significantly improve DNNs prediction performance by trading-off coverage. In this paper we propose a method to construct a selective classifier given a trained neural network. Our method allows a user to set a desired risk level. At test time, the classifier rejects instances as needed, to grant the desired risk (with high probability). Empirical results over CIFAR and ImageNet convincingly demonstrate the viability of our method, which opens up possibilities to operate DNNs in mission-critical applications. For example, using our method an unprecedented 2% error in top-5 ImageNet classification can be guaranteed with probability 99.9%, with almost 60% test coverage.read more
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ImageNet Large Scale Visual Recognition Challenge
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Proceedings ArticleDOI
Very deep convolutional neural network based image classification using small training sample size
Shuying Liu,Weihong Deng +1 more
TL;DR: In this article, a modified VGG-16 network was used to fit CIFAR-10 without severe overfitting and achieved 8.45% error rate on the dataset.
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An optimum character recognition system using decision functions
TL;DR: The character recognition problem, usually resulting from characters being corrupted by printing deterioration and/or inherent noise of the devices, is considered from the viewpoint of statistical decision theory and the optimum recogition is obtained.