Solutions for a Better Tomorrow

Generative Adversarial Networks - Collaborative Artistic Production with Machine Learning and Automation Generative Adversarial Networks - Collaborative Artistic Production with Machine Learning and Automation

Generative Adversarial Networks - Collaborative Artistic Production with Machine Learning and Automation

While artificial intelligence has been successfully applied in the visual arts, the training of algorithms to generate sounds and music in the waveform domain has yet to be fully explored. The project “Collaborative Artistic Production with Generative Adversarial Networks” explores the use of machine learning and automation in creative practice and its potential to change human creative and artistic processes. The project specifically uses Generative Adversarial Networks (GANs) to investigate how basic artistic principles such as form, function, and aesthetics might change due to the introduction of a semi-autonomous system of generation.

14-generative-adversarial-networks 14-generative-adversarial-networks

The project is divided into four separate research stages, each involving its own set of sub-questions:

1st stage/
examines how GAN systems can function as artistic tools for the creation of 3D forms, and how reversible encodings of these forms can be used to experiment with traditional semiotic systems.

2nd stage/
examines GANs’ ability to discern patterns and embedded information in a diverse data set of hand tools spanning human history.

3rd stage/
applies a GAN system to a music ecosystem in an attempt to blur the boundary between the human creator and the generative system in a live performance context.

4th stage/
takes advantage of cultural studies and ethnographic methods to examine how the use of these technologies can be resolved more broadly within historical models of the artist and the artwork.

The project is a significant exploration of the intersection between technology and artistic agency, and its potential to enhance the creative process and produce new and innovative works of art.

Project Team
  • Professor Peter Nelson, Associate Director (Teaching and Learning) and Associate Professor, Academy of Visual Arts
  • Professor Roberto Alonso Trillo, Associate Professor, Directed Studies Coordinator, Academy of Music
  • Professor François Mouillot, Assistant Professor, Department of Humanities and Creative Writing
Interested in Our Innovation?

Contact us at iit@hkbu.edu.hk to explore collaboration and partnership opportunities.

Back