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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
Number of pages8
Publication statusAcceptance date - 20 Mar 2018
EventIEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 -
Duration: 18 Jun 201822 Jun 2018


ConferenceIEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018


  • Deep Neural Networks
  • Generative Models

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  • Projects

    EPSRC Centre for Doctoral Training in Digital Entertainment

    Willis, P.


    Project: Research-related funding

    Cite this

    Dorta, G., Vicente, S., Agapito, L., Campbell, N., & Simpson, I. (Accepted/In press). Structured Uncertainty Prediction Networks. Paper presented at IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018, .