Dataset Diversity Metrics and Impact on Classification Models

2026-03-16

AI Summary

This study addresses the lack of a unified definition and quantification of training dataset diversity, which hinders accurate assessment of its impact on model robustness. Focusing on medical imaging, the work presents the first systematic comparison between reference-free and semantic diversity metrics. Leveraging the MorphoMNIST and PadChest datasets, the authors conduct a multidimensional analysis—integrating Fréchet Inception Distance (FID), AUC, semantic diversity measures, controlled perturbations, and clinical expert evaluations—to examine how diversity correlates with expert intuition, downstream performance, and training dynamics. The findings reveal that FID and semantic diversity more effectively predict model performance, whereas merely increasing the number of imaging device sources can inadvertently encourage models to rely on non-robust shortcut features, thereby exposing a critical pitfall in data diversity design.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

The diversity of training datasets is usually perceived as an important aspect to obtain a robust model. However, the definition of diversity is often not defined or differs across papers, and while some metrics exist, the quantification of this diversity is often overlooked when developing new algorithms. In this work, we study the behaviour of multiple dataset diversity metrics for image, text and metadata using MorphoMNIST, a toy dataset with controlled perturbations, and PadChest, a publicly available chest X-ray dataset. We evaluate whether these metrics correlate with each other but also with the intuition of a clinical expert. We also assess whether they correlate with downstream-task performance and how they impact the training dynamic of the models. We find limited correlations between the AUC and image or metadata reference-free diversity metrics, but higher correlations with the FID and the semantic diversity metrics. Finally, the clinical expert indicates that scanners are the main source of diversity in practice. However, we find that the addition of another scanner to the training set leads to shortcut learning. The code used in this study is available at https://github.com/TheoSourget/dataset_diversity_evaluation

Problem

• dataset diversity
• diversity metrics
• classification models
• training dynamics
• shortcut learning

Innovation

• dataset diversity metrics
• reference-free evaluation
• shortcut learning
• FID
• semantic diversity