A generative adversarial network (GAN) is a machine learning (ML) model in which two neural networks compete by using deep learning methods to become more accurate in their predictions. GANs typically run unsupervised and use a cooperative zero-sum game framework to learn.

The two neural networks that make up a GAN are referred to as the generator and the discriminator. The generator is a convolutional neural network, and the discriminator is a deconvolutional neural network. The goal of the generator is to artificially manufacture outputs that could easily be mistaken for real data. The goal of the discriminator is to identify which of the outputs it receives have been artificially created.

Essentially, generative models create their own training data. While the generator is trained to produce false data, the discriminator network is taught to distinguish between the generator's manufactured data and true examples. If the discriminator rapidly recognizes the fake data that the generator produces -- such as an image that isn't a human face -- the generator suffers a penalty. As the feedback loop between the adversarial networks continues, the generator begins to produce higher-quality and more believable output, and the discriminator becomes better at flagging data that has been artificially created. For instance, a generative adversarial network can be trained to create realistic-looking images of human faces that don't belong to any real person.