Understanding GAN Architecture Explained #ai #artificialintelligence #machinelearning #aiagent

Generative Adversarial Networks are comprised of two main components: a generator and a discriminator. The generator creates synthetic data, while the discriminator evaluates this data against real data, aiming to distinguish between the two. This adversarial process drives the system to improve continuously. Think of the generator as a forger trying to create counterfeit money, and the discriminator as the detective trying to catch the forgeries. Over time, the generator gets better at creating realistic data as the discriminator sharpens its skills in detection. This dynamic feedback loop is what makes GANs so powerful in generating high-quality synthetic data.