Stability In Gan Training: Balancing Techniques And Performance Evaluation
TL;DR
A critical and comparative review of ten research papers, each offering a unique solution to the core issues of GAN training instability, indicate that no single method dominates across all scenarios, but hybrid combinations lead to the best generalizability and stability under constraints like limited data, non IID settings, or real-time computation.
Abstract
Generative Adversarial Networks (GANs) have become the backbone of data synthesis across multiple domains like healthcare, audio processing, real-time systems, and federated learning. Despite their revolutionary potential, GANs suffer from major challenges in stability, scalability, and data dependence. This seminar presents a critical and comparative review of ten research papers, each offering a unique solution to the core issues of GAN training instability. The reviewed methods encompass domain adaptation, feature distillation, theoretical regularization, adaptive augmentation, and hierarchical federated architectures. This report provides an in-depth literature analysis, followed by a cross-domain metric-wise performance comparison. The results indicate that no single method dominates across all scenarios, but hybrid combinations lead to the best generalizability and stability under constraints like limited data, non IID settings, or real-time computation. Findings are contextualized in terms of practical impact, with implications for the future design of robust, scalable GAN architectures.