cGAN: Conditional Generative Adversarial Network -- How to Gain Control Over GAN Outputs

Have you experimented with Generative Adversarial Networks (GANs) yet? If so, you may have encountered a situation where you wanted your GAN to generate a specific type of data but did not have sufficient control over GANs outputs. For example, assume you used a broad spectrum of flower images to train a GAN capable of producing fake pictures of flowers. While you can use your model to generate an image of a random flower, you cannot instruct it to create an image of, say, a tulip or a sunflower. Conditional GAN (cGAN) allows us to condition the network with additional information such as class labels.