Researches Advanced in Generative Adversarial Networks and Their Applications for Image-Generating NFT

Xiaolin Guo

doi:10.54097/hset.v39i.6562

Authors

Xiaolin Guo

DOI:

https://doi.org/10.54097/hset.v39i.6562

Keywords:

Image-Generating NFT; Human Art and Machine Art; Autonomous Computer Generation of Artworks.

Abstract

A generative adversarial network is a deep learning model, an unsupervised learning method. In computer vision, the generative adversarial network is a research direction with rapid development in recent years; Similarly, the rise of cryptocurrency Non-Fungible Tokens (NFT) in recent years has also attracted much attention to the field of art. As an "irreplaceable currency" NFT provides a more novel and convenient way for content creators and artists to create and increases the continuous income of original creators. At the same time, it has also attracted widespread attention to the financial field. Therefore, this paper is determined to combine the generative adversarial network of the production of NFT and discuss and analyze the autonomous computer generation of artworks. Firstly, this paper starts with the model's structure, the design of the objective function, Block chain technology, and Irreplaceable tokens encrypted using blockchain technology. Then, the image generated by the whole generative adversarial network and transformed into NFT works are described in detail. In addition, this paper briefly discusses the development ethics of human art and machine art and the prospects for its development trend.

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