This is the first installment in a two-part series about generative adversarial networks. The term refers to two neural networks a generator and a discriminator competing against each other to succeed in a game. The object of the game is for the generator to fool the discriminator with examples that look similar to the training set. In semisupervised learning, the algorithm (discriminator) has one set of examples labeled as truth and one set that is not labeled as a noise noise.”]
Source: https://securityintelligence.com/generative-adversarial-networks-and-cybersecurity-part-1/