Sensoy, M;
Saleki, M;
Julier, S;
Aydogan, R;
Reid, J;
(2021)
Misclassification Risk and Uncertainty Quantification in Deep Classifiers.
In:
Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV) 2021.
(pp. pp. 2483-2491).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a classifier’s predictions and reduce the likelihood of acting on erroneous predictions. The second is a novel way to train the classifier such that erroneous classifications are biased towards less risky categories. We combine these two approaches in a principled way. While doing this, we extend evidential deep learning with pignistic probabilities, which are used to quantify uncertainty of classification predictions and model rational decision making under uncertainty.We evaluate the performance of our approach on several image classification tasks. We demonstrate that our approach allows to (i) incorporate misclassification cost while training deep classifiers, (ii) accurately quantify the uncertainty of classification predictions, and (iii) simultaneously learn how to make classification decisions to minimize expected cost of classification errors.
| Type: | Proceedings paper |
|---|---|
| Title: | Misclassification Risk and Uncertainty Quantification in Deep Classifiers |
| Event: | The Winter Conference on Applications of Computer Vision (WACV) 2021 |
| ISBN-13: | 978-1-6654-0477-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/WACV48630.2021.00253 |
| Publisher version: | https://doi.org/10.1109/WACV48630.2021 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Training, Deep learning, Computer vision, Uncertainty, Conferences, Decision making, Predictive models |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10132169 |
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