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Structured Uncertainty Prediction Networks

Dorta, G; Vicente, S; Agapito, L; Campbell, NDF; Simpson, I; (2018) Structured Uncertainty Prediction Networks. In: (Proceedings) 31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (pp. pp. 5477-5485). IEEE: Salt Lake City, UT, USA. Green open access

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Abstract

This paper is the first work to propose a network to predict a structured uncertainty distribution for a synthesized image. Previous approaches have been mostly limited to predicting diagonal covariance matrices [15]. Our novel model learns to predict a full Gaussian covariance matrix for each reconstruction, which permits efficient sampling and likelihood evaluation. We demonstrate that our model can accurately reconstruct ground truth correlated residual distributions for synthetic datasets and generate plausible high frequency samples for real face images. We also illustrate the use of these predicted covariances for structure preserving image denoising.

Type: Proceedings paper
Title: Structured Uncertainty Prediction Networks
Event: 31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Location: Salt Lake City, UT
Dates: 18 June 2018 - 23 June 2018
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/CVPR.2018.00574
Publisher version: https://doi.org/10.1109/CVPR.2018.00574
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: Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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/10074871
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