| Autor: |
Alberti, Michele, Pondenkandath, Vinaychandran, Vögtlin, Lars, Würsch, Marcel, Ingold, Rolf, Liwicki, Marcus |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
6th Swiss Conference on Data Science (SDS), Bern, Switzerland, 2019 |
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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