Advancements and challenges in the development of generative adversarial network (GANs) for deep learning

Abstract Generative adversarial networks (GANs) have reshaped modern deep learning by enabling the creation of high-fidelity synthetic data. This survey distils a decade of progress while adding three fresh dimensions. First, we propose a unified three-layer taxonomy–linking divergence choice, objective loss, and architecture family–that clarifies how theoretical tweaks ripple through training dynamics. Second, we deliver the field’s most comprehensive comparison of neural-architecture-search-driven GANs, benchmarking evolutionary, differentiable, and reinforcement approaches on CIFAR-10 and STL-10 to expose the cost-versus-quality frontier. Within this framework we revisit the classic hurdles of mode collapse, vanishing gradients, and instability, showing how re-engineered backbones and carefully chosen divergences mitigate them. We analyse key evaluation metrics–including Inception Score, Fréchet Inception Distance, and Kernel Inception Distance–and explain when each one truly matters. The survey then tracks GAN adoption across computer vision, natural-language processing, music generation, medical imaging, time-series forecasting, urban-planning simulation, and imbalanced-data classification, highlighting both successes and persistent gaps. We close by outlining open problems: scaling NAS-designed GANs to higher resolutions, integrating domain-specific priors, and developing training routines with stronger convergence guarantees. Together, these insights offer researchers and engineers a clear roadmap for pushing GAN technology beyond its current limits.