Generative Adversarial Networks (GANs) are a class of artificial intelligence algorithms designed for unsupervised learning. Introduced by Ian Goodfellow in 2014, GANs consist of two neural networks: the generator and the discriminator. The generator creates synthetic data, while the discriminator evaluates the authenticity of the data. These networks compete against each other, leading to the creation of highly realistic synthetic data.
