SPRITZ-PS: validation of synthetic face images using a large dataset of printed documents

TL;DR: This paper proposed an alternative generator architecture for GANs, borrowing from style transfer literature, which leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images.

TL;DR: A generative adversarial networks algorithm designed to solve the generative modeling problem and its applications in medicine, education and robotics are studied.

TL;DR: Deep convolutional generative adversarial networks (DCGANs) as discussed by the authors learn a hierarchy of representations from object parts to scenes in both the generator and discriminator for unsupervised learning.

TL;DR: The idea is to learn a function that maps input patterns into a target space such that the L/sub 1/ norm in the target space approximates the "semantic" distance in the input space.