Active Divergence with Generative Deep Learning - A Survey and Taxonomy

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Broad, Terence; Berns, Sebastian; Colton, Simon and Grierson, Mick. 2021. 'Active Divergence with Generative Deep Learning - A Survey and Taxonomy'. In: 12th International Conference on Computational Creativity, ICCC’21. Mexico City, Mexico 14 - 18 September 2021. [Conference or Workshop Item]

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Official URL: https://computationalcreativity.net/iccc21/proceed...

Abstract or Description

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.

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Conference or Workshop Item (Paper)

Additional Information:

Copyright notice: all contents of these proceedings by ACC, the Association for Computational Creativity, published under a Creative Commons Attribution (CC BY) license, which allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (https://creativecommons.org/licenses/by/4.0/).

Keywords:

active divergence, generative deep learning, novelty

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