eprintid: 10132169
rev_number: 14
eprint_status: archive
userid: 608
dir: disk0/10/13/21/69
datestamp: 2021-08-04 16:33:52
lastmod: 2021-10-05 00:37:39
status_changed: 2021-08-04 16:33:52
type: proceedings_section
metadata_visibility: show
creators_name: Sensoy, M
creators_name: Saleki, M
creators_name: Julier, S
creators_name: Aydogan, R
creators_name: Reid, J
title: Misclassification Risk and Uncertainty Quantification in Deep Classifiers
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Training, Deep learning, Computer vision,
Uncertainty, Conferences, Decision making, Predictive models
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
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.
date: 2021
date_type: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
official_url: https://doi.org/10.1109/WACV48630.2021
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1873423
doi: 10.1109/WACV48630.2021.00253
isbn_13: 978-1-6654-0477-8
lyricists_name: Julier, Simon
lyricists_id: SJULI23
actors_name: Julier, Simon
actors_id: SJULI23
actors_role: owner
full_text_status: public
publication: WACV
pagerange: 2483-2491
event_title: The Winter Conference on Applications of Computer Vision (WACV) 2021
book_title: Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV) 2021
citation:        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)       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10132169/1/Sensoy_Misclassification_Risk_and_Uncertainty_Quantification_in_Deep_Classifiers_WACV_2021_paper.pdf