Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/59650

Sharing Open Deep Learning Models

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Item Summary

Title:Sharing Open Deep Learning Models
Authors:DALGALI, Ayse
Crowston, Kevin
Keywords:Collective Intelligence and Crowds
Digital and Social Media
Deep learning, Model sharing, Transfer learning
Date Issued:08 Jan 2019
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/59650
ISBN:978-0-9981331-2-6
DOI:10.24251/HICSS.2019.256
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Collective Intelligence and Crowds


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