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Sharing Open Deep Learning Models

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dc.contributor.author DALGALI, Ayse
dc.contributor.author Crowston, Kevin
dc.date.accessioned 2019-01-03T00:00:34Z
dc.date.available 2019-01-03T00:00:34Z
dc.date.issued 2019-01-08
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59650
dc.description.abstract We examine how and why trained deep learning (DL) models are shared, and by whom, and why some developers share their models while others do not. Prior research has examined sharing of data and software code, but DL models are a hybrid of the two. The results from a Qualtrics survey administered to GitHub users and academics who publish on DL show that a diverse population shares DL models, from students to computer/data scientists. We find that motivations for sharing include: increasing citation rates; contributing to the collaboration of developing new DL models; encouraging to reuse; establishing a good reputation; receiving feedback to improve the model; and personal enjoyment. Reasons for not sharing include: lack of time; thinking that their models would not be interesting for others; and not having permission for sharing. The study contributes to our understanding of motivations for participating in a novel form of peer-production.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Collective Intelligence and Crowds
dc.subject Digital and Social Media
dc.subject Deep learning, Model sharing, Transfer learning
dc.title Sharing Open Deep Learning Models
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2019.256
Appears in Collections: Collective Intelligence and Crowds


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