27 citations · 67 across the 10 of their papers we have counts for
12 papers
Feature Encoding in VAE-based Audio Decoders: Effects of Input, Depth and Distribution
Louis McCallum, Mick Grierson
Neural audio synthesis models like the Realtime Audio Variational autoEncoder (RAVE) achieve impressive genera tion quality, yet how their internal representations encode musical f…
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…
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…
Calligraphic Stylisation Learning with a Physiologically Plausible Model of Movement and Recurrent Neural Networks
Daniel Berio, Memo Akten, Frederic Fol Leymarie +2
We propose a computational framework to learn stylisation patterns from example drawings or writings, and then generate new trajectories that possess similar stylistic qualities. W…
Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks
Memo Akten, Mick Grierson
We investigate a human-machine collaborative drawing environment in which an autonomous agent sketches images while optionally allowing a user to directly influence the agent's tra…
Real-time interactive sequence generation and control with Recurrent Neural Network ensembles
Memo Akten, Mick Grierson
Recurrent Neural Networks (RNN), particularly Long Short Term Memory (LSTM) RNNs, are a popular and very successful method for learning and generating sequences. However, current g…