From Mode Collapse to Quantum Generation: Architectural Innovations, Evaluation Dilemmas, and Multimodal Fusion Paths for Generative Adversarial Networks (GAN)

Abstract

In this paper, based on papers from the last five years, this paper systematically sorts out the technical evolution, application expansion and existing challenges of Generative Adversarial Networks (GAN). Generative Adversarial Networks (GANs), as the core framework of deep generative models, have shown revolutionary potential in the fields of image synthesis, time-series prediction, and cross-modal generation in the past decade. For example, the variants represented by Wasserstein GAN and StyleGAN have significantly improved the generation quality and training stability through theoretical optimization and architectural innovation, and have been successfully applied to interdisciplinary scenarios, such as medical image synthesis, financial time series generation, and quantum computing. However, at the same time, the development of GAN is still limited by deep conflicts such as uncontrollable training dynamics, fragmented evaluation indexes, ethical security risks and high resource consumption. The aim of this paper is to provide researchers with a critical view of technology evolution through a panoramic analysis and to call for the construction of a next-generation generative framework that balances efficacy, security and sustainability.