Generative Adversarial Networks (GANs)

A machine learning architecture consisting of two neural networks competing against each other—a generator creating synthetic data and a discriminator evaluating its authenticity—resulting in increasingly realistic generated content. The generator attempts to create data indistinguishable from real examples whilst the discriminator becomes better at detecting fakes, driving both to improve through adversarial training. Businesses can apply GANs for creating synthetic training data, generating product variations, enhancing low-resolution images, and creating realistic customer personas for testing. Common applications include generating diverse product images, creating synthetic customer data for testing systems, and producing high-quality marketing visuals.