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arXiv · 1906.04736

Improving Reproducible Deep Learning Workflows with DeepDIVA

Michele Alberti·Vinaychandran Pondenkandath·Lars Vögtlin·Marcel Würsch·Rolf Ingold·Marcus Liwicki
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Abstract

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.

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Michele Alberti, Vinaychandran Pondenkandath, Lars Vögtlin, Marcel Würsch, Rolf Ingold, Marcus Liwicki. 2019-06-11. Improving Reproducible Deep Learning Workflows with DeepDIVA. https://arxiv.org/abs/1906.04736

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