A Comprehensive Review of Generative Adversarial Networks in Deep Learning: Emerging Applications and Hybrid Architectures

Abstract

Generative adversarial networks (GANs) have fundamentally transformed the deep learning field since they enable the creation of synthetic data that closely matches real world data. This paper presents a comprehensive and up‐to‐date review of GANs variants and their revolutionary impact in several domains, and major developments in the new applications. We demonstrate the GANs architecture, versatility of GANs variants such as CycleGAN, bidirectional generative adversarial networks (BiGAN), quantum‐enhanced GAN (Q‐GAN) and StyleGAN. In this work, different GANs variants are analysed and combined with the proposed improvements that enhance stability and performance. Despite their outstanding performance, GANs suffer from instability and mode collapse during training. This paper examines various strategies and improvements to enhance GANs stability and performance, including hybrid architectures that integrate GANs with other deep learning models and practical utility in domain‐specific expert systems. Moreover, in the context of deepfakes, it considers the ethical and legal reflections of GANs generated content. We also contribute to a comprehensive perspective on current applications and future prospects of GANs, underscoring their versatility and promise in areas such as image synthesis, medical imaging, anomaly detection, code synthesis and urban simulation.