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arXiv · 1805.05396

Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes

Tongfei Chen·Jiří Navrátil·Vijay Iyengar·Karthikeyan Shanmugam
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Abstract

We propose a novel confidence scoring mechanism for deep neural networks based on a two-model paradigm involving a base model and a meta-model. The confidence score is learned by the meta-model observing the base model succeeding/failing at its task. As features to the meta-model, we investigate linear classifier probes inserted between the various layers of the base model. Our experiments demonstrate that this approach outperforms various baselines in a filtering task, i.e., task of rejecting samples with low confidence. Experimental results are presented using CIFAR-10 and CIFAR-100 dataset with and without added noise. We discuss the importance of confidence scoring to bridge the gap between experimental and real-world applications.

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Tongfei Chen, Jiří Navrátil, Vijay Iyengar, Karthikeyan Shanmugam. 2018-05-14. Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes. https://arxiv.org/abs/1805.05396

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