An evaluation framework for synthetic data generation models
Ioannis E. Livieris Nikos Alimpertis George Domalis Dimitris Tsakalidis
Nowadays, the use of synthetic data has gained popularity as a cost-efficient
strategy for enhancing data augmentation for improving machine learning models
performance as well as addressing concerns related to sensitive data privacy.
Therefore, the necessity of ensuring quality of generated synthetic data, in
terms of accurate representation of real data, consists of primary importance.
In this work, we present a new framework for evaluating synthetic data
generation models' ability for developing high-quality synthetic data. The
proposed approach is able to provide strong statistical and theoretical
information about the evaluation framework and the compared models' ranking.
Two use case scenarios demonstrate the applicability of the proposed framework
for evaluating the ability of synthetic data generation models to generated
high quality data. The implementation code can be found in
https://github.com/novelcore/synthetic_data_evaluation_framework.