no code implementations • 5 Feb 2024 • Nick Collins, Mick Grierson
As future musical AIs adhere closely to human music, they may form their own attachments to particular human artists in their databases, and these biases may in the worst case lead to potential existential threats to all musical history.
no code implementations • 25 Aug 2023 • Luís Arandas, Mick Grierson, Miguel Carvalhais
Deep generative models produce data according to a learned representation, e. g. diffusion models, through a process of approximation computing possible samples.
no code implementations • 12 Jul 2021 • Terence Broad, Sebastian Berns, Simon Colton, Mick Grierson
Generative deep learning systems offer powerful tools for artefact generation, given their ability to model distributions of data and generate high-fidelity results.
no code implementations • 9 Jul 2020 • 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.
1 code implementation • 25 May 2020 • Terence Broad, Frederic Fol Leymarie, Mick Grierson
This results in the meaningful manipulation of sets of features that correspond to the generation of a broad array of semantically significant features of the generated images.
no code implementations • 28 Feb 2020 • Memo Akten, Rebecca Fiebrink, Mick Grierson
The exploration of these representations acts as a metaphor for the process of developing a visual understanding and/or visual vocabulary of the world.
no code implementations • 27 Feb 2020 • 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.
no code implementations • 17 Feb 2020 • 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.
no code implementations • 6 Oct 2019 • 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 distribution with novel characteristics.
no code implementations • 6 Oct 2019 • Terence Broad, Mick Grierson
This paper details a developing artistic practice around an ongoing series of works called (un)stable equilibrium.
no code implementations • 24 Sep 2017 • Daniel Berio, Memo Akten, Frederic Fol Leymarie, Mick Grierson, Réjean Plamondon
We propose a computational framework to learn stylisation patterns from example drawings or writings, and then generate new trajectories that possess similar stylistic qualities.
no code implementations • 14 Dec 2016 • 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.
no code implementations • 14 Dec 2016 • 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 trajectory.