Work

Explainable evaluation of generative adversarial networks for wearables data augmentation

Year: 2025

Type: article

Abstract: Data augmentation represents an opportunity for Artificial Intelligence applications, as it aims at creating new synthetic data based on an existing baseline. In this paper, we present a new evaluation framework for Generative Adversarial Networks (GANs), a data augmentation technique, in multivariate data classification contexts. The goal is not limited to assess the performance variations obtained through GANs, but also to inspect results with explainable AI (XAI) tools, understanding how GANs work and, finally, exploiting them to discover new knowledge. To this aim, we adopt the Logic Learning Machine for performance assessment and rule extraction, and introduce a new measure of rule similarity to compare different artificial datasets. We apply the methodology on two case studies, activity recognition and physical fatigue detection, confirming that GANs can help in overcoming limitations of original datasets and lead to new discoveries.

Source: Engineering Applications of Artificial Intelligence

DOI: 10.1016/j.engappai.2025.110133

Authors: Sara Narteni, Vanessa Orani, Enrico Ferrari, Damiano Verda, Enrico Cambiaso, Maurizio Mongelli

Institutions: Politecnico di Torino, Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Aitek (Italy)

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,200

Funders: European Commission, Ministero dell'Istruzione e del Merito