Generative Adversarial Networks (GANs) are a type of neural network that can be used for generating new images or texts by learning from a training dataset

GANs consist of two neural networks: a generator and a discriminator.

The generator takes in random noise as input and generates new images or texts.

The discriminator takes in both real and generated images or texts and tries to distinguish between them.

The two networks are trained together in a game-like manner

The generator tries to fool the discriminator, while the discriminator tries to accurately classify the generated images or texts.

GANs can produce high-quality images and texts that are difficult to distinguish from real ones.

GANs have been used for a wide range of applications, like generating realistic images, video game characters and dance movements.