The Bartlett, University College London, London, United Kingdom.
Neural Netw. 2023 Aug;165:420-434. doi: 10.1016/j.neunet.2023.05.028. Epub 2023 May 30.
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.
生成对抗网络 (GAN) 被提出作为一种从原始版本生成多个副本的方法，该方法结合了鉴别器和生成器。GAN 的主要应用是音频和视频内容的随机生成。GAN 作为一种生成个体群体的神经方法，已经模拟了基于生物启发算子（如突变、交叉和选择）的遗传算法。本文提出了具有与 GAN 相同特征和功能的深度学习生成对抗随机神经网络 (RNN)。此外，所提出的算法针对数字创意应用，在数据市场中生成可交易的副本，例如 1D 函数或音频、2D 和 3D 图像以及视频内容。RNN 生成器创建从潜在空间映射的个体，而 GAN 鉴别器根据真实数据分布对其进行评估。已经针对具有不同维度的多个输入向量以及 1D 函数和 2D 图像评估了深度学习生成对抗 RNN 的性能。所呈现的结果是成功的：RNN 生成器的学习目标以低错误率创建可交易的副本，而 RNN 鉴别器的学习目标则识别不合适的个体。
