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.
@article{arxiv.1906.04736,
title = {Improving Reproducible Deep Learning Workflows with DeepDIVA},
author = {Michele Alberti and Vinaychandran Pondenkandath and Lars Vögtlin and Marcel Würsch and Rolf Ingold and Marcus Liwicki},
journal= {arXiv preprint arXiv:1906.04736},
year = {2019}
}