Short paper by MSCD graduate Erik Ulberg on 'Hand-Crafting Neural Networks for Art-Making' will appear at ICCC'20 – DaraghByrne.me

Daragh Byrne Associate Teaching Professor
School of Architecture, Carnegie Mellon University. Core faculty for MSCD and PhD CD.
Courtesy appointments in the School of Design and the Human Computer Interaction Institute.
Afflilated facilty with the IDeATe network, Block Center for Technology and Society, and CyLab.
Co-Lead of the TRACES Lab. Co-founder and platform lead for a2ru's Ground Works.
Pronunciation: Dah-rah (silent ‘gh’) · Pronouns: he/him. · Google Scholar · ResearchGate · ORCID 0000-0001-7193-006X.

News » Short paper by MSCD graduate Erik Ulberg on 'Hand-Crafting Neural Networks for Art-Making' will appear at ICCC'20

July 20, 2020

Erik Ulberg, Daniel Cardoso Llach, Daragh Byrne. ‘Hand-Crafting Neural Networks for Art-Making’. In Proc. Eleventh International Conference on Computational Creativity (ICCC’20) September 7 – 11, 2020, Coimbra, Portugal.

A growing number of visual artists use neural networks in their practice. While these networks show promise as an art form, the lack of interpretability limits control to high level decisions based on observations. As an alternative, this research investigates the hand-crafting of network weights coupled with explanatory visual- izations as a form of creative control over the inter- nal and lower level processes. Two experimental tools were developed: one for parametrically generating first layer kernels and the second for editing multiple lay- ers. These tools attempt to transform the hand-crafting of features into “crafting” in a richer sense by bringing network weights and visual materials into a tight feed- back loop. The first author extensively engaged with these tools and these case studies serve to examine the affordances of internal interaction for art-making. The findings suggest that direct manipulation can be used intentionally and can yield insights into network representations, but that hand-crafting networks of greater sophistication would likely require a hybrid approach integrating data-driven methods.

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