Hand-Crafting Neural Networks for Art-Making

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 visualizations as a form of creative control over the internal and lower level processes. Two experimental tools were developed: one for parametrically generating first layer kernels and the second for editing multiple layers. 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 feedback 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.