1 citations · 1 across the 2 of their papers we have counts for
4 papers
Establishing baselines and introducing TernaryMixOE for fine-grained out-of-distribution detection
Noah Fleischmann, Walter Bennette, Nathan Inkawhich
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 conf…
Generalized Adversarial Distances to Efficiently Discover Classifier Errors
Walter Bennette, Sally Dufek, Karsten Maurer +2
Given a black-box classification model and an unlabeled evaluation dataset from some application domain, efficient strategies need to be developed to evaluate the model. Random sam…
Harnessing Adversarial Distances to Discover High-Confidence Errors
Walter Bennette, Karsten Maurer, Sean Sisti
Given a deep neural network image classification model that we treat as a black box, and an unlabeled evaluation dataset, we develop an efficient strategy by which the classifier c…
Facility Locations Utility for Uncovering Classifier Overconfidence
Karsten Maurer, Walter Bennette
Assessing the predictive accuracy of black box classifiers is challenging in the absence of labeled test datasets. In these scenarios we may need to rely on a human oracle to evalu…