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Improving Reproducible Deep Learning Workflows with DeepDIVA

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Alberti Michele, Pondenkandath Vinaychandran, Vögtlin Lars, Würsch Marcel, Ingold Rolf, Liwicki Marcus,
Project HisDoc III : Large-Scale Historical Document Classification
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Original article (peer-reviewed)

Journal 2019 6th Swiss Conference on Data Science (SDS)
Page(s) 13 - 18
Title of proceedings 2019 6th Swiss Conference on Data Science (SDS)
DOI 10.1109/sds.2019.00-14


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.