Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities
This work addresses the challenge of enhancing creative responsibility in generative systems for artists and computational creativity researchers, but it appears incremental as it builds on existing automated machine learning concepts.
The paper tackles the problem of automating generative deep learning for artistic purposes by proposing a framework that aims to increase creative autonomy in generative systems, framing user-system interaction as a co-creative process.
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.