Generative adversarial networks for synthetic data generation: A systematic review of techniques, applications, and evaluation methods

Generative adversarial networks (GANs), which have emerged as one of the powerful frameworks for generating synthetic data, have proven remarkably capable across domains. This systematic review explores the rapidly evolving GAN landscape, particularly their applications for generating high-fidelity synthetic data that resemble real-world datasets' statistical properties. We comprehensively analyze recent literature to present the following key findings: 1. GANs' Capabilities: GANs have demonstrated significant potential across various fields, especially in creating synthetic data that mimic real-world datasets. 2. State-of-the-Art Architectures: Advanced GAN variants, such as Conditional GANs, Wasserstein GANs, and Cycle GANs, have shown great promise for transformation in sectors like healthcare, finance, and image processing. 3. Evaluation Methodologies: Metrics for assessing GAN-generated data include statistical similarity, downstream task performance, and privacy preservation, highlighting strengths and limitations in current evaluation paradigms. 4. Training Difficulties: GANs face challenges such as mode collapse, instability, and sensitivity to hyperparameters, which require further innovation and exploration. Additionally, we critically examine the methodologies used to evaluate the quality and utility of GAN-generated data. Metrics like statistical similarity, downstream task performance, and privacy preservation provide a broad view of current strengths and limitations. Besides synthetic data generation using GAN-based methods, this review discusses training difficulties and emerging directions aimed at mitigating issues like mode collapse, instability, and hyperparameter sensitivity. The findings emphasize significant progress in GAN-based synthetic data generation but underline the need for a robust, standardized evaluation framework and continued innovation in model architectures. 1. Robust Evaluation Framework: Developing a standardized evaluation framework for GAN-generated data is essential for advancing the field. 2. Model Architecture Innovation: Ongoing innovation in model architectures is necessary to overcome current limitations and enhance GAN performance. 3. Synthetic Data Generation: GANs hold great potential for generating synthetic data, which can address data privacy concerns, data scarcity, and data augmentation needs. This review aims to help researchers and practitioners understand the current state and future directions of GAN applications in synthetic data generation.