Active Divergence with Generative Deep Learning -- A Survey and Taxonomy

• Main Authors: Broad, Terence, Berns, Sebastian, Colton, Simon, Grierson, Mick
• Format: Journal Article
• Language: English
• Published: 12-07-2021
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Learning
• DOI: 10.48550/arxiv.2107.05599

Generative deep learning systems offer powerful tools for artefact
generation, given their ability to model distributions of data and generate
high-fidelity results. In the context of computational creativity, however, a
major shortcoming is that they are unable to explicitly diverge from the
training data in creative ways and are limited to fitting the target data
distribution. To address these limitations, there have been a growing number of
approaches for optimising, hacking and rewriting these models in order to
actively diverge from the training data. We present a taxonomy and
comprehensive survey of the state of the art of active divergence techniques,
highlighting the potential for computational creativity researchers to advance
these methods and use deep generative models in truly creative systems.