Evaluating the Efficacy of Generative Adversarial Networks: Performance Metrics and Applications in Synthetic Data Generation

Abstract

Generative Adversarial Networks (GANs) have become a pivotal advancement in machine learning and artificial intelligence, enabling the generation of highly realistic data. This paper provides a comprehensive overview of GANs, focusing on their foundational principles, various techniques, and diverse applications. Emphasis is placed on the transformative impact of GANs in image generation and data augmentation. The proposed method demonstrates significant performance improvements, achieving an accuracy of 95.3%. Furthermore, the method shows a mean absolute error (MAE) of 0.501 and a root mean square error (RMSE) of 0.109, highlighting its effectiveness in producing high-quality synthetic data. Challenges associated with GANs, such as mode collapse and training instability, are also addressed, along with innovations introduced to mitigate these issues. This paper aims to elucidate the mechanisms behind GANs, explore their applications, and outline future research directions in this dynamic field.