An evaluation framework for synthetic data generation models
This work addresses the need for reliable evaluation methods in synthetic data generation, which is crucial for data augmentation and privacy in machine learning, but it appears incremental as it builds on existing evaluation concepts.
The authors tackled the problem of evaluating synthetic data generation models by proposing a new framework that provides statistical and theoretical insights and ranks models, demonstrating its applicability in two use-case scenarios.
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.