Improving Reproducible Deep Learning Workflows with DeepDIVA

Alberti, Michele; Pondenkandath, Vinaychandran; Vogtlin, Lars; Gygli, Marcel; Ingold, Rolf; Liwicki, Marcus (14 June 2019). Improving Reproducible Deep Learning Workflows with DeepDIVA In: 2019 6th Swiss Conference on Data Science (SDS) (pp. 13-18). New York: IEEE 10.1109/SDS.2019.00-14

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The field of deep learning is experiencing a trend towards producing reproducible research. Nevertheless, it is still often a frustrating experience to reproduce scientific results. This is especially true in the machine learning community, where it is considered acceptable to have black boxes in your experiments. We present DeepDIVA, a framework designed to facilitate easy experimentation and their reproduction. This framework allows researchers to share their experiments with others, while providing functionality that allows for easy experimentation, such as: boilerplate code, experiment management, hyper-parameter optimization, verification of data integrity and visualization of data and results. Additionally, the code of DeepDIVA is well-documented and supported by several tutorials that allow a new user to quickly familiarize themselves with the framework.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	Business School > Institute for Public Sector Transformation > Data and Infrastructure Business School
Name:	Alberti, Michele; Pondenkandath, Vinaychandran; Vogtlin, Lars; Gygli, Marcel; Ingold, Rolf and Liwicki, Marcus
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ISBN:	978-1-7281-3105-4
Publisher:	IEEE
Language:	English
Submitter:	Marcel Gygli
Date Deposited:	10 Oct 2023 15:52
Last Modified:	10 Oct 2023 15:52
Publisher DOI:	10.1109/SDS.2019.00-14
ARBOR DOI:	10.24451/arbor.20053
URI:	https://arbor.bfh.ch/id/eprint/20053