Conditional GAN is a generative adversarial network whose Generator and Discriminator are conditioned during training by using some additional information. This auxiliary information could be, in theory, anything, such as a class label, a set of tags, or even a written description.
During CGAN training, the Generator learns to produce realistic examples for each label in the training dataset, and the Discriminator learns to distinguish fake example-label pairs from real example-label pairs.
