Structured Uncertainty Prediction Networks

Garoe Dorta, Sara Vicente, Lourdes Agapito, Neill Campbell, Ivor Simpson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)
104 Downloads (Pure)

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.
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.
Original languageEnglish
Title of host publication2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
PublisherIEEE
Pages5477-5485
Number of pages9
ISBN (Electronic)9781538664209
ISBN (Print)9781538664216
DOIs
Publication statusPublished - 17 Dec 2018
EventIEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 -
Duration: 18 Jun 201822 Jun 2018

Publication series

NameIEEE/CVF Conference on Computer Vision and Pattern Recognition
PublisherIEEE
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018
Period18/06/1822/06/18

Keywords

  • Deep Neural Networks
  • Generative Models

Cite this