7 citations · 16 across the 4 of their papers we have counts for
4 papers · 1 filter
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…
Transforming the output of GANs by fine-tuning them with features from different datasets
Terence Broad, Mick Grierson
In this work we present a method for fine-tuning pre-trained GANs with features from different datasets, resulting in the transformation of the output distribution into a new distr…
Searching for an (un)stable equilibrium: experiments in training generative models without data
Terence Broad, Mick Grierson
This paper details a developing artistic practice around an ongoing series of works called (un)stable equilibrium. These works are the product of using modern machine toolkits to t…