DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments
This provides a tool for researchers in machine learning and document analysis to improve reproducibility, though it is incremental as it builds on existing frameworks like PyTorch.
The authors tackled the problem of reproducibility in machine learning experiments by introducing DeepDIVA, a Python framework that enables quick setup and sharing of reproducible experiments with integrated analysis functions, demonstrated through case studies in handwritten document analysis.
We introduce DeepDIVA: an infrastructure designed to enable quick and intuitive setup of reproducible experiments with a large range of useful analysis functionality. Reproducing scientific results can be a frustrating experience, not only in document image analysis but in machine learning in general. Using DeepDIVA a researcher can either reproduce a given experiment with a very limited amount of information or share their own experiments with others. Moreover, the framework offers a large range of functions, such as boilerplate code, keeping track of experiments, hyper-parameter optimization, and visualization of data and results. To demonstrate the effectiveness of this framework, this paper presents case studies in the area of handwritten document analysis where researchers benefit from the integrated functionality. DeepDIVA is implemented in Python and uses the deep learning framework PyTorch. It is completely open source, and accessible as Web Service through DIVAServices.