GAN is a network where two models, a generative model G and a discriminatory model D, are trained simultaneously. The generative model will be trained to produce new bedroom images by capturing the data distribution associated with the training dataset. The discriminatory model will be trained to correctly classify a given input image as real (i.e. coming from the training dataset images) or fake (i.e. synthetic image produced by the generative model). Simply put, the discriminatory model is a typical CNN image classifier model, or more specifically, a binary image classifier.