Diversity in Stable GANs: A Systematic Review of Mode Collapse Mitigation Strategies

Abstract

Mode collapse poses a critical challenge in training generative adversarial networks (GANs), particularly in applications such as medical imaging, where diverse and clinically relevant outputs are essential. This systematic review methodically examines the causes and impacts of mode collapse, classifies mitigation strategies into four categories; architectural modifications, loss function adaptations, regularization techniques, and hybrid techniques, and evaluates their effectiveness. Hybrid approaches, combining adversarial loss adaptation, architectural modifications, and regularization terms, are particularly promising. Additionally, integrating GANs with frameworks such as federated learning, diffusion models, and attention mechanisms shows potential to improve stability and diversity. By highlighting successful strategies and identifying gaps, especially in domain-specific contexts such as medical imaging, this review aims to advance GAN applications in low-resource regions and beyond, improving healthcare and other critical sectors.