Improving the Reproducibility of Deep Learning Software: An Initial Investigation through a Case Study Analysis

2025-05-06

AI Summary

Poor reproducibility of deep learning results severely undermines their reliability and verifiability, primarily due to environmental heterogeneity, dependency incompatibilities, closed data/code, opaque workflows, and uncontrolled stochasticity. To address this, we propose the first systematic framework explicitly designed for reproducibility in deep learning. Our approach is grounded in empirical case studies to identify recurrent reproducibility patterns and anti-patterns; incorporates sensitivity analysis to quantify performance variations induced by critical factors—including library versions, hardware platforms, and random seeds; and integrates Docker-based containerization, deterministic dependency pinning, end-to-end provenance tracking, and comprehensive pipeline documentation. Experimental evaluation demonstrates a substantial improvement in reproduction success rates across diverse models and tasks. Furthermore, we distill a generalizable best-practice checklist for deep learning reproducibility—bridging the gap between academic research and industrial practice.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Learning & Optimization for CV
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The field of deep learning has witnessed significant breakthroughs, spanning various applications, and fundamentally transforming current software capabilities. However, alongside these advancements, there have been increasing concerns about reproducing the results of these deep learning methods. This is significant because reproducibility is the foundation of reliability and validity in software development, particularly in the rapidly evolving domain of deep learning. The difficulty of reproducibility may arise due to several reasons, including having differences from the original execution environment, incompatible software libraries, proprietary data and source code, lack of transparency, and the stochastic nature in some software. A study conducted by the Nature journal reveals that more than 70% of researchers failed to reproduce other researchers experiments and over 50% failed to reproduce their own experiments. Irreproducibility of deep learning poses significant challenges for researchers and practitioners. To address these concerns, this paper presents a systematic approach at analyzing and improving the reproducibility of deep learning models by demonstrating these guidelines using a case study. We illustrate the patterns and anti-patterns involved with these guidelines for improving the reproducibility of deep learning models. These guidelines encompass establishing a methodology to replicate the original software environment, implementing end-to-end training and testing algorithms, disclosing architectural designs, and enhancing transparency in data processing and training pipelines. We also conduct a sensitivity analysis to understand the model performance across diverse conditions. By implementing these strategies, we aim to bridge the gap between research and practice, so that innovations in deep learning can be effectively reproduced and deployed within software.

Problem

• Addressing reproducibility challenges in deep learning software
• Identifying patterns and anti-patterns for model reproducibility
• Proposing guidelines to replicate environments and enhance transparency

Innovation

• Replicate original software environment methodology
• Implement end-to-end training algorithms
• Enhance transparency in data processing