Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities

Sebastian Berns*, Terence Broad, Christian Guckelsberger, Simon Colton

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Abstract

We present a framework for automating generative deep learning with a specific focus on artistic applications. The framework provides opportunities to hand over creative responsibilities to a generative system as targets for automation. For the definition of targets, we adopt core concepts from automated machine learning and an analysis of generative deep learning pipelines, both in standard and artistic settings. To motivate the framework, we argue that automation aligns well with the goal of increasing the creative responsibility of a generative system, a central theme in computational creativity research. We understand automation as the challenge of granting a generative system more creative autonomy, by framing the interaction between the user and the system as a co-creative process. The development of the framework is informed by our analysis of the relationship between automation and creative autonomy. An illustrative example shows how the framework can give inspiration and guidance in the process of handing over creative responsibility.
Original languageEnglish
Title of host publicationProceedings of the 12th International Conference on Computational Creativity (ICCC 2021)
PublisherAssociation for Computational Creativity
Pages357-366
ISBN (Electronic)978-989-54160-3-5
Publication statusPublished - 1 Sept 2021
MoE publication typeA4 Conference publication
EventInternational Conference on Computational Creativity - Mexico City, Mexico
Duration: 14 Sept 202118 Sept 2021

Conference

ConferenceInternational Conference on Computational Creativity
Abbreviated titleICCC
Country/TerritoryMexico
CityMexico City
Period14/09/202118/09/2021

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