An evaluation framework for synthetic data generation models

arXiv:2404.08866v1 Announce Type: new
Abstract: 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. …

arxiv cs.ai cs.lg data evaluation framework synthetic synthetic data type