Sharing open deep learning models

Ayse Dalgali, Kevin Crowston

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

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.
Original languageEnglish (US)
Title of host publicationProceedings of the 52nd Hawaii International Conference on System Sciences
Volume52
StatePublished - 2019

Fingerprint

Deep learning
Students
Feedback

Cite this

Dalgali, A., & Crowston, K. (2019). Sharing open deep learning models. In Proceedings of the 52nd Hawaii International Conference on System Sciences (Vol. 52)

Sharing open deep learning models. / Dalgali, Ayse; Crowston, Kevin.

Proceedings of the 52nd Hawaii International Conference on System Sciences. Vol. 52 2019.

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Dalgali, A & Crowston, K 2019, Sharing open deep learning models. in Proceedings of the 52nd Hawaii International Conference on System Sciences. vol. 52.
Dalgali A, Crowston K. Sharing open deep learning models. In Proceedings of the 52nd Hawaii International Conference on System Sciences. Vol. 52. 2019
Dalgali, Ayse ; Crowston, Kevin. / Sharing open deep learning models. Proceedings of the 52nd Hawaii International Conference on System Sciences. Vol. 52 2019.
@inbook{80bbd2944adb4a48911e0853372da89d,
title = "Sharing open deep learning models",
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.",
author = "Ayse Dalgali and Kevin Crowston",
year = "2019",
language = "English (US)",
volume = "52",
booktitle = "Proceedings of the 52nd Hawaii International Conference on System Sciences",

}

TY - CHAP

T1 - Sharing open deep learning models

AU - Dalgali, Ayse

AU - Crowston, Kevin

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

M3 - Chapter (peer-reviewed)

VL - 52

BT - Proceedings of the 52nd Hawaii International Conference on System Sciences

ER -