Reproducible Pattern Recognition Research: The Case of Optimistic SSL

TL;DR

This paper explores the process and challenges of making pattern recognition research, specifically on optimistic semi-supervised learning, fully reproducible by detailing methods, tools, and trade-offs involved.

Contribution

It presents a comprehensive approach to achieving reproducibility in pattern recognition research, including definitions, tools, and analysis setup, with practical examples.

Findings

Reproducibility enhances transparency and validation in pattern recognition research.

Tools and clear analysis setup are crucial for reproducibility.

Alternative analyses demonstrate the flexibility enabled by reproducible code.

Abstract

In this paper, we discuss the approaches we took and trade-offs involved in making a paper on a conceptual topic in pattern recognition research fully reproducible. We discuss our definition of reproducibility, the tools used, how the analysis was set up, show some examples of alternative analyses the code enables and discuss our views on reproducibility.