27 citations · 58 across the 7 of their papers we have counts for
9 papers
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…
Guru, Partner, or Pencil Sharpener? Understanding Designers' Attitudes Towards Intelligent Creativity Support Tools
Angus Main, Mick Grierson
Creativity Support Tools (CST) aim to enhance human creativity, but the deeply personal and subjective nature of creativity makes the design of universal support tools challenging.…
Network Bending: Expressive Manipulation of Deep Generative Models
Terence Broad, Frederic Fol Leymarie, Mick Grierson
We introduce a new framework for manipulating and interacting with deep generative models that we call network bending. We present a comprehensive set of deterministic transformati…
Learning to See: You Are What You See
Memo Akten, Rebecca Fiebrink, Mick Grierson
The authors present a visual instrument developed as part of the creation of the artwork Learning to See. The artwork explores bias in artificial neural networks and provides mecha…
Deep Meditations: Controlled navigation of latent space
Memo Akten, Rebecca Fiebrink, Mick Grierson
We introduce a method which allows users to creatively explore and navigate the vast latent spaces of deep generative models. Specifically, our method enables users to \textit{disc…
Amplifying The Uncanny
Terence Broad, Frederic Fol Leymarie, Mick Grierson
Deep neural networks have become remarkably good at producing realistic deepfakes, images of people that (to the untrained eye) are indistinguishable from real images. Deepfakes ar…