Sharing Open Deep Learning Models

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dc.contributor.authorDALGALI, Ayse
dc.contributor.authorCrowston, Kevin
dc.date.accessioned2019-01-03T00:00:34Z
dc.date.available2019-01-03T00:00:34Z
dc.date.issued2019-01-08
dc.description.abstractWe 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.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.256
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59650
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCollective Intelligence and Crowds
dc.subjectDigital and Social Media
dc.subjectDeep learning, Model sharing, Transfer learning
dc.titleSharing Open Deep Learning Models
dc.typeConference Paper
dc.type.dcmiText

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