Improving Reproducible Deep Learning Workflows with DeepDIVA
| Autor: | Rolf Ingold, Michele Alberti, Lars Vögtlin, Vinaychandran Pondenkandath, Marcus Liwicki, Marcel Wursch |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning business.industry Computer science Deep learning Boilerplate code Machine Learning (stat.ML) Field (computer science) Visualization Machine Learning (cs.LG) Data visualization Workflow Statistics - Machine Learning Data integrity Task analysis Artificial intelligence Software engineering business |
| Zdroj: | SDS |
| DOI: | 10.48550/arxiv.1906.04736 |
| 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: | OpenAIRE |
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