Deep Learning for Graphics

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dc.contributor.authorMitra, Niloy J.en_US
dc.contributor.authorRitschel, Tobiasen_US
dc.contributor.authorKokkinos, Iasonasen_US
dc.contributor.authorGuerrero, Paulen_US
dc.contributor.authorKim, Vladimiren_US
dc.contributor.authorRematas, Konstantinosen_US
dc.contributor.authorYumer, Ersinen_US
dc.contributor.editorRitschel, Tobias and Telea, Alexandruen_US
dc.date.accessioned2018-04-14T18:36:35Z
dc.date.available2018-04-14T18:36:35Z
dc.date.issued2018
dc.description.abstractIn computer graphics, many traditional problems are now better handled by deep-learning based data-driven methods. In applications that operate on regular 2D domains, like image processing and computational photography, deep networks are state-of-the-art, beating dedicated hand-crafted methods by significant margins. More recently, other domains such as geometry processing, animation, video processing, and physical simulations have benefited from deep learning methods as well. The massive volume of research that has emerged in just a few years is often difficult to grasp for researchers new to this area. This tutorial gives an organized overview of core theory, practice, and graphics-related applications of deep learning.en_US
dc.description.sectionheadersTutorials
dc.description.seriesinformationEG 2018 - Tutorials
dc.identifier.doi10.2312/egt.20181029
dc.identifier.issn1017-4656
dc.identifier.pages13-15
dc.identifier.urihttps://doi.org/10.2312/egt.20181029
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egt20181029
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectNeural networks
dc.subjectComputer graphics
dc.titleDeep Learning for Graphicsen_US
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