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Found 4 papers, 2 papers with code
Facility Locations Utility for Uncovering Classifier Overconfidence
Assessing the predictive accuracy of black box classifiers is challenging in the absence of labeled test datasets.
Harnessing Adversarial Distances to Discover High-Confidence Errors
Through rigorous empirical experimentation, we demonstrate that our Adversarial Distance search discovers high-confidence errors at a rate greater than expected given model confidence.
Generalized Adversarial Distances to Efficiently Discover Classifier Errors
In this paper we propose a generalization to the Adversarial Distance search that leverages concepts from adversarial machine learning to identify predictions for which a classifier may be overly confident.
Establishing baselines and introducing TernaryMixOE for fine-grained out-of-distribution detection
Machine learning models deployed in the open world may encounter observations that they were not trained to recognize, and they risk misclassifying such observations with high confidence.
