A conceptual essay maps ambiguity, uncertainty, and indeterminacy onto machine learning creative practice and proposes half-trained latent spaces as the richest source of new form.
Visual Indeterminacy in GAN Art
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abstract
This paper explores visual indeterminacy as a description for artwork created with Generative Adversarial Networks (GANs). Visual indeterminacy describes images which appear to depict real scenes, but, on closer examination, defy coherent spatial interpretation. GAN models seem to be predisposed to producing indeterminate images, and indeterminacy is a key feature of much modern representational art, as well as most GAN art. It is hypothesized that indeterminacy is a consequence of a powerful-but-imperfect image synthesis model that must combine general classes of objects, scenes, and textures.
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Creative Loss: Ambiguity, Uncertainty and Indeterminacy
A conceptual essay maps ambiguity, uncertainty, and indeterminacy onto machine learning creative practice and proposes half-trained latent spaces as the richest source of new form.