Generative adversarial nets (GAN)s are responsible for significant progress in image generation since first introduced by [[Ian Goodfellow]] in 2015. GAN is used for image interpolation, modification of attributes, change image style, and other image modification tasks. GANs pair a generator model and discriminator model to learn a distribution iteratively. The discriminator model learns a decision boundary between the output of the generator model and real examples. The decision boundary (but not the real examples) is fed back to the generator model to create increasingly realistic outputs, and the discriminator learns a new decision boundary. Over time, the distribution of generated examples becomes increasingly like the distribution of real examples. From Goodfellow's paper, "The generative model can be thought of as analogous to a team of counterfeiters, trying to produce fake currency and use it without detection, while the discriminative model is analogous to the policy, trying to detect the counterfeit currency." [[Cycle GAN]]