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

The paper proposes artistic and computational approaches to investigate the ability of machine learning to synthesize 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 the artwork of Pablo Picasso, the paper also aims at observing the image manipulation being generated by GAN in order to understand and compare machine vision with human vision through the means of collage and artwork creation. And finally, to explore the process of seeing based on the phenomenology of embodiment, trying to understand how the objects could be visible to us through the machine and artificial intelligence without being ‘bodily involved in the world’.