  As neural network classifiers are deployed in real-world applications, it is
crucial that their failures can be detected reliably. One practical solution is
to assign confidence scores to each prediction, then use these scores to filter
out possible misclassifications. However, existing confidence metrics are not
yet sufficiently reliable for this role. This paper presents a new framework
that produces a quantitative metric for detecting misclassification errors.
This framework, RED, builds an error detector on top of the base classifier and
estimates uncertainty of the detection scores using Gaussian Processes.
Experimental comparisons with other error detection methods on 125 UCI datasets
demonstrate that this approach is effective. Further implementations on two
probabilistic base classifiers and two large deep learning architecture in
vision tasks further confirm that the method is robust and scalable. Third, an
empirical analysis of RED with out-of-distribution and adversarial samples
shows that the method can be used not only to detect errors but also to
understand where they come from. RED can thereby be used to improve
trustworthiness of neural network classifiers more broadly in the future.
