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

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Generative Adversarial Networks (GANs) are established for content generation, mimicking biological evolution.
• GANs combine Discriminator and Generator models for creating realistic data replicas.
• Existing GAN applications primarily focus on audio and video generation.

Purpose of the Study:

• Introduce a Deep Learning Generative Adversarial Random Neural Network (RNN) with GAN-like capabilities.
• Propose the RNN for the Digital Creative application to generate tradeable replicas in a Data Marketplace.
• Evaluate the RNN's performance in generating diverse data types, including 1D functions and 2D images.

Main Methods:

• The proposed RNN model integrates Generator and Discriminator components, similar to GANs.
• The RNN Generator maps individuals from a latent space.
• The RNN Discriminator evaluates generated individuals against true data distribution.

Main Results:

• The RNN Generator successfully creates tradeable replicas with minimal error.
• The RNN Discriminator effectively identifies unfit individuals.
• Performance was validated across various input vector dimensions, 1D functions, and 2D images.

Conclusions:

• The Deep Learning Generative Adversarial RNN demonstrates successful content generation capabilities.
• The RNN is suitable for applications requiring the creation of tradeable digital assets.
• This research advances generative models for data marketplaces and creative content generation.