Generative Adversarial Networks for Synthetic Data Generation in Deep Learning Applications

Abstract

Generative Adversarial Networks (GANs) have emerged as a transformative approach for synthetic data generation in deep learning, addressing critical challenges such as data scarcity, privacy concerns, and algorithmic bias. This synthesis review provides a comprehensive analysis of GANs' role in creating high-fidelity synthetic data across diverse domains, including healthcare, finance, computer vision, and natural language processing. By leveraging an adversarial training process involving a generator and discriminator, GANs effectively capture complex data distributions, producing realistic synthetic samples that enhance model robustness and generalization. The review explores foundational GAN principles, advanced architectures like DCGANs, cGANs, CycleGANs, and TimeGANs, and their applications in generating medical images, financial time-series, and tabular data. It also discusses the advantages of GANs, such as privacy preservation and cost-efficiency, alongside limitations, including training instability, mode collapse, and the lack of standardized evaluation metrics. Comparative analysis with other methods like Variational Autoencoders and traditional statistical approaches highlights GANs' superior realism for complex data types. Future research directions include improving training stability, developing robust evaluation benchmarks, and integrating privacy-enhancing techniques. This review underscores GANs' potential to revolutionize deep learning applications while emphasizing the need for ethical guidelines to mitigate misuse risks.