Evolution and Breakthroughs of Generative Adversarial Network Technology

School of Data Science, The Chinese University of Hong Kong, Shenzhen, Shenzhen City, Guangdong Province

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Abstract

Generative Adversarial Networks (GANs) have significantly evolved since their introduction, continually adapting through theoretical and architectural innovations to remain a vibrant research area in generative AI. This survey comprehensively analyzes the developmental trajectory of GAN technology, highlighting key improvements from the original adversarial framework to recent state-of-the-art models. This paper examines foundational milestones including DCGAN, which introduced convolutional architectures for enhanced image quality; WGAN, which improved training stability via the Wasserstein distance; StyleGAN, which enabled fine-grained style-based generation; and the recently proposed R3GAN, which achieves superior performance through a theoretically- motivated and trick-free design. Through comparative analysis of metrics such as FID, Inception Score, and mode coverage on standard datasets, this paper demonstrates how successive models have addressed inherent challenges like mode collapse and instability. By synthesizing these advances, this review clarifies the logical progression of GAN research and affirms that ongoing innovation sustains GANs as a competitive and dynamically improving approach for generative modeling.