Generating Realistic Data With Gans

Generative Adversarial Networks (GANs) are a class of deep learning models that have gained immense popularity in recent years due to their ability to synthesize realistic data. GANs consist of two neural networks, a generator and a discriminator, which are trained in a zero-sum game framework. The generator takes a random noise vector as input and outputs a synthetic data sample, while the discriminator takes a data sample as input and outputs a scalar value representing the probability of the sample being real.