Generative Adversarial Networks (GANs) are a class of machine learning models designed to generate new data samples that resemble a given training dataset. Introduced by Ian Goodfellow and his colleagues in 2014, GANs consist of two neural networks: the generator and the discriminator. The generator creates synthetic data, while the discriminator evaluates its authenticity against real data. Generative Adversarial Networks represent a cutting-edge frontier in artificial intelligence, poised to reshape multiple industries. By pitting a generator against a discriminator, GANs generate data that closely mimics real-world examples. This technology is being harnessed to create photorealistic images, advance virtual reality experiences, and even restore damaged artworks. Get in touch with us today!