What are Generative Adversarial Networks (GANs)?

GANs (Generative Adversarial Networks) are a type of AI architecture consisting of two neural networks - a “generator” and a “discriminator” - that compete against each other in a process to create realistic synthetic data. The generator tries to create fake data (like images, audio, or text) that appears real, while the discriminator tries to distinguish between real and generated samples. Through this adversarial training, the generator progressively improves at creating increasingly convincing outputs until the discriminator can no longer tell the difference.