24 citations · 105 across the 14 of their papers we have counts for
14 papers
Exploring XAI for the Arts: Explaining Latent Space in Generative Music
Nick Bryan-Kinns, Berker Banar, Corey Ford +4
Explainable AI has the potential to support more interactive and fluid co-creative AI systems which can creatively collaborate with people. To do this, creative AI models need to b…
Towards Mode Balancing of Generative Models via Diversity Weights
Sebastian Berns, Simon Colton, Christian Guckelsberger
Large data-driven image models are extensively used to support creative and artistic work. Under the currently predominant distribution-fitting paradigm, a dataset is treated as gr…
Trash to Treasure: Using text-to-image models to inform the design of physical artefacts
Amy Smith, Hope Schroeder, Ziv Epstein +3
Text-to-image generative models have recently exploded in popularity and accessibility. Yet so far, use of these models in creative tasks that bridge the 2D digital world and the c…
The Beyond the Fence Musical and Computer Says Show Documentary
Simon Colton, Maria Teresa Llano, Rose Hepworth +11
During 2015 and early 2016, the cultural application of Computational Creativity research and practice took a big leap forward, with a project where multiple computational systems…
Explainable Computational Creativity
Maria Teresa Llano, Mark d'Inverno, Matthew Yee-King +4
Human collaboration with systems within the Computational Creativity (CC) field is often restricted to shallow interactions, where the creative processes, of systems and humans ali…
Active Divergence with Generative Deep Learning -- A Survey and Taxonomy
Terence Broad, Sebastian Berns, Simon Colton +1
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…