13 citations · 27 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…
Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities
Sebastian Berns, Terence Broad, Christian Guckelsberger +1
We present a framework for automating generative deep learning with a specific focus on artistic applications. The framework provides opportunities to hand over creative responsibi…
Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search
Alexander Hagg, Sebastian Berns, Alexander Asteroth +2
We consider multi-solution optimization and generative models for the generation of diverse artifacts and the discovery of novel solutions. In cases where the domain's factors of v…