Generative Adversarial Network Algorithms in Art: Machine Vision in Generation of Collage Art

Abstract

The paper proposes artistic and computational approaches to investigate the ability of matching learning to synthesise and manipulate the image dataset into artwork creation. By using the Generative Adversarial Network (GAN), it is observed how the machine algorithms are able to learn artistic styles and manipulate relevant pictures to generate digital artifacts, in particular, the images generated through latent space interpolation. Referring to an artwork of Pablo Picasso, the paper also aims at observing the collages being generated by GAN in order to understand and compare the machine vision with human vision in collage and artwork creation. And finally, to explore the process of seeing through the phenomenology of embodiment, trying to understand how the objects could be visible to us through the machine and artificial intelligence without being "bodily involvement in the world".

Keywords: Artificial Intelligence, Machine Learning, Generative Adversarial Network, GAN, Art Creation, Collage

DOI: 10.54941/ahfe1002778