Practical Machine Learning for Rendering: From Research to Deployment
| dc.contributor.author | Marshall, Carl S. | en_US |
| dc.contributor.author | Vembar, Deepak S. | en_US |
| dc.contributor.author | Ganguly, Sujoy | en_US |
| dc.contributor.author | Guinier, Florent | en_US |
| dc.contributor.editor | Hahmann, Stefanie | en_US |
| dc.contributor.editor | Patow, Gustavo A. | en_US |
| dc.date.accessioned | 2022-04-22T11:46:01Z | |
| dc.date.available | 2022-04-22T11:46:01Z | |
| dc.date.issued | 2022 | |
| dc.identifier.isbn | 978-3-03868-172-4 | |
| dc.identifier.issn | 1017-4656 | |
| dc.identifier.uri | https://doi.org/10.2312/egt.20221059 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/egt20221059 | |
| dc.description.abstract | Applying machine learning to improve graphics rendering or asset pipelines is challenging. Practicalities such as proprietary datasets, network retraining, and deployment issues make it difficult to translate published research into deployed solutions. In this course, industry practitioners at the forefront of this interdisciplinary field discuss and outline potential solutions. | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.title | Practical Machine Learning for Rendering: From Research to Deployment | en_US |
| dc.description.seriesinformation | Eurographics 2022 - Tutorials | |
| dc.description.sectionheaders | Tutorials | |
| dc.identifier.doi | 10.2312/egt.20221059 |
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