How do generative adversarial networks (GANs) achieve high-quality image synthesis, and what are the key factors that influence their training stability and output quality?

GANs achieve high-quality image synthesis by pitting two neural networks—a generator and a discriminator—against each other in a zero-sum game. The generator creates images, while the discriminator evaluates their authenticity. Key factors influencing GAN training stability and output quality include balancing the training between the generator and discriminator, using techniques like spectral normalization to stabilize training, and implementing architecture enhancements like Progressive GANs and StyleGAN to improve image fidelity and diversity.