Harnessing Adversarial Distances to Discover High-Confidence Errors
- URL: http://arxiv.org/abs/2006.16055v1
- Date: Mon, 29 Jun 2020 13:44:16 GMT
- Title: Harnessing Adversarial Distances to Discover High-Confidence Errors
- Authors: Walter Bennette, Karsten Maurer, Sean Sisti
- Abstract summary: We investigate the problem of finding errors at rates greater than expected given model confidence.
We propose a query-efficient and novel search technique that is guided by adversarial perturbations.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 can be evaluated. Randomly sampling and
labeling instances from an unlabeled evaluation dataset allows traditional
performance measures like accuracy, precision, and recall to be estimated.
However, random sampling may miss rare errors for which the model is highly
confident in its prediction, but wrong. These high-confidence errors can
represent costly mistakes, and therefore should be explicitly searched for.
Past works have developed search techniques to find classification errors above
a specified confidence threshold, but ignore the fact that errors should be
expected at confidence levels anywhere below 100\%. In this work, we
investigate the problem of finding errors at rates greater than expected given
model confidence. Additionally, we propose a query-efficient and novel search
technique that is guided by adversarial perturbations to find these mistakes in
black box models. Through rigorous empirical experimentation, we demonstrate
that our Adversarial Distance search discovers high-confidence errors at a rate
greater than expected given model confidence.
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