
Generative Adversarial Networks (GANs) have emerged as a groundbreaking force in artificial intelligence. These complex systems pit two models against one another: a generator, which crafts new data instances, and a discriminator, which evaluates their authenticity. This dynamic process enables GANs to produce remarkably refined outputs, pushing the boundaries of AI’s creative and analytical capabilities. Their versatility has wide applications,










