A Stable GAN Training Framework Using Pretrained Parameters

This study proposes a parameter pre-training method that enhances the stability of Generative Adversarial Networks (GANs) through the pre-adjustment and dynamic control of batch size and epochs. Unlike conventional approaches in which parameters are tuned during the main training phase, the proposed method first conducts pre-training, thereby improving convergence and enabling more efficient data generation. Experimental results demonstrate that, compared to random search, the proposed approach reduces the average loss values of the generator and discriminator by more than 5%, achieving generator and discriminator losses of 39.4% and 38.2%, respectively. These findings indicate that stable data generation through GAN pre-training can be effectively applied to diverse fields of appropriate technology, including medical image enhancement, environmental data reconstruction, and technical support in resource-limited regions. Accordingly, this research not only contributes to advancing the stability of deep generative models but also highlights the potential integration of artificial intelligence with appropriate technology for creating social value.