CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12701 > Article
Paper
7 June 2023 Face attribute classification with evidential deep learning
Arin Zeyneloglu, Sara Atito Ali Ahmed, Berrin Yanikoglu
Author Affiliations +
Proceedings Volume 12701, Fifteenth International Conference on Machine Vision (ICMV 2022); 127011M (2023) https://doi.org/10.1117/12.2680634
Event: Fifteenth International Conference on Machine Vision (ICMV 2022), 2022, Rome, Italy
Abstract
We address the problem of uncertainty quantification in the domain of face attribute classification, using Evidential Deep Learning (EDL) framework. The proposed EDL approach leverages the strength of Convolution Neural Networks (CNN), with the objective of representing the uncertainty in the output predictions. Predominantly, the softmax/sigmoid activation functions are applied to map the output logits of the CNN to target class probabilities in multi-class classification problems. By replacing the standard softmax/sigmoid output of a CNN with the parameters of the evidential distribution, EDL learns to represent the uncertainty in its predictions. The proposed approach is evaluated on CelebA and LFWA datasets. The quantitative and qualitative analysis demonstrate the suitability and strength of EDL to estimate the uncertainty in the output predictions without hindering the accuracy of CNN-based models.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Arin Zeyneloglu, Sara Atito Ali Ahmed, and Berrin Yanikoglu "Face attribute classification with evidential deep learning", Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 127011M (7 June 2023); https://doi.org/10.1117/12.2680634
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 8 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Deep learning
Neural networks
Uncertainty analysis
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top