Projects per year
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.
|Title of host publication||2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition|
|Number of pages||9|
|Publication status||Published - 17 Dec 2018|
|Event||IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 - |
Duration: 18 Jun 2018 → 22 Jun 2018
|Name||IEEE/CVF Conference on Computer Vision and Pattern Recognition|
|Conference||IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018|
|Period||18/06/18 → 22/06/18|
- Deep Neural Networks
- Generative Models
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1/04/14 → 30/09/22
Project: Research-related funding
Cosker, D., Bilzon, J., Campbell, N., Cazzola, D., Colyer, S., Fincham Haines, T., Hall, P., Kim, K. I., Lutteroth, C., McGuigan, P., O'Neill, E., Richardt, C., Salo, A., Seminati, E., Tabor, A. & Yang, Y.
1/09/15 → 28/02/21
Project: Research council
- Department of Computer Science - Reader
- Centre for the Analysis of Motion, Entertainment Research & Applications
- EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa)
- UKRI CDT in Accountable, Responsible and Transparent AI
- Centre for Autonomous Robotics (CENTAUR)
- Centre for Mathematics and Algorithms for Data (MAD)
- Artificial Intelligence
- Visual Computing
Person: Research & Teaching