Mitra, N;
Ritschel, T;
Kokkinos, I;
Guerrero, P;
Kim, V;
Rematas, K;
Yumer, E;
(2018)
Deep Learning for Graphics.
In: Ritschel, T and Telea, A, (eds.)
Eurographics 2018 - Tutorials.
(pp. pp. 13-15).
Eurographics Association
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Abstract
In computer graphics, many traditional problems are now better handled by deep-learning based data-driven methods. In an increasing variety of problem settings, deep networks are state-of-the-art, beating dedicated hand-crafted methods by significant margins. This tutorial gives an organized overview of core theory, practice, and graphics-related applications of deep learning.
| Type: | Proceedings paper |
|---|---|
| Title: | Deep Learning for Graphics |
| Event: | Eurographics 2018, 39th Eurographics Conference, 16-20 April 2018, Delft, Netherlands |
| Location: | Delft, Netherlannds |
| Dates: | 16 April 2018 - 20 April 2018 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.2312/egt.20181029 |
| Publisher version: | https://doi.org/10.2312/egt.20181029 |
| Language: | English |
| Additional information: | © 2018 The Author(s). Eurographics Proceedings © 2018 The Eurographics Association. - This is the published version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL 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 UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10058875 |
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