Exploring the Role of Generative Adversarial Networks (GANs) and Generative AI for Synthetic Data Generation and Augmentation in Machine Learning

Abstract

However the world has entered into a realm where data is a boon and burden, and that is where GANs and Generative AI come to create synthetic data. Utilizing an adversarial process, they generate realistic, privacy-preserving datasets that increase model robustness while alleviating data scarcity and overfitting. The study presents a systematic evaluation of these techniques as in contrast with traditional augmentation methods, according to their ability to keep statistical integrity while minimizing bias. In a world where the line between genuine data and manufactured data becomes blurred, GANs furnish the potential for AI paradigms to flourish in data-strapped territories as boundaries turn to novelty and contours turn to machine-learning Feng Shui.