The Deep Learning Generative Adversarial Random Neural Network in data marketplaces: The digital creative

Abstract

Generative Adversarial Networks (GANs) have been proposed as a method to generate multiple replicas from an original version combining a Discriminator and a Generator. The main applications of GANs have been the casual generation of audio and video content. GANs, as a neural method that generates populations of individuals, have emulated genetic algorithms based on biologically inspired operators such as mutation, crossover and selection. This article presents the Deep Learning Generative Adversarial Random Neural Network (RNN) with the same features and functionality as a GAN. Furthermore, the presented algorithm is proposed for an application, the Digital Creative, that generates tradeable replicas in a Data Marketplace, such as 1D functions or audio, 2D and 3D images and video content. The RNN Generator creates individuals mapped from a latent space while the GAN Discriminator evaluates them based on the true data distribution. The performance of the Deep Learning Generative Adversarial RNN has been assessed against several input vectors with different dimensions, in addition to 1D functions and 2D images. The presented results are successful: the learning objective of the RNN Generator creates tradeable replicas at low error, whereas the RNN Discriminator learning target identifies unfit individuals.