# Improving Reproducible Deep Learning Workflows with DeepDIVA

## TL;DR

DeepDIVA is a comprehensive framework that simplifies conducting, sharing, and reproducing deep learning experiments, addressing reproducibility challenges in the machine learning community.

## Contribution

It introduces a new framework with features like experiment management, hyper-parameter tuning, and data verification to improve reproducibility and ease of use in deep learning research.

## Key findings

- Facilitates reproducible deep learning experiments
- Includes tools for hyper-parameter optimization and data verification
- Provides extensive documentation and tutorials

## 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.