Evaluating Generative Adversarial Networks Performance in Image Synthesis with Graphical Analysis of Loss Functions and Quality Metrics

Generative Adversarial Networks (GANs) have revolutionized image synthesis by using two neural networks, a generator and a discriminator, to create realistic images from random noise. In this adversarial process, the generator attempts to fool the discriminator, which distinguishes between real and fake images. Advanced variants like conditional GANs and CycleGANs enable tasks like specified image generation and style transfer. Our paper presents improvements in image quality and training stability for GANs by introducing new training methods and loss function modifications to address issues like mode collapse. Evaluated on CelebA and CIFAR-10, our model outperforms previous GANs with Inception Scores of 9.69 and 10.79 and Frechet Inception Distances of 7.91 and -9.69, respectively. These results demonstrate better convergence, more stable training, and higher-quality image generation, establishing a new benchmark for GAN research and applications.