Monitoring and Mitigating Mode Collapse in GANs using KL Divergence and Jensen-Shannon Divergencebased Diversity Metrics

Generative Adversarial Networks (GANs) have emerged as powerful models for generating high-quality synthetic data across diverse areas, like image synthesis, text generation, and data augmentation. However, challenges such as mode collapse, unstable training dynamics, and inefficient resource utilization hinder their widespread deployment. This paper proposes an optimized GAN framework that enhances training stability, accelerates convergence, and improves the quality of generated samples. The methodology incorporates adaptive learning rate scheduling, improved loss functions, and a robust regularization mechanism to mitigate mode collapse and ensure stable training. Additionally, we introduce a systematic evaluation of model performance using Jensen- Shannon Divergence (JSD) to quantify the resemblance between real and generated distributions over multiple training epochs. The experimental results on a datasets depicts that the approach significantly reduces JSD values over time, indicating improved sample diversity and distribution alignment. Comparative analysis with standard GAN architectures further validates the effectiveness of our optimization techniques. These findings enhance the development of GAN applications across fields such as computer vision, natural language processing, and scientific simulations.