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Found 13 papers, 1 papers with code
Avoiding an AI-imposed Taylor's Version of all music history
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.
Antagonising explanation and revealing bias directly through sequencing and multimodal inference
Deep generative models produce data according to a learned representation, e. g. diffusion models, through a process of approximation computing possible samples.
Active Divergence with Generative Deep Learning -- A Survey and Taxonomy
Generative deep learning systems offer powerful tools for artefact generation, given their ability to model distributions of data and generate high-fidelity results.
Guru, Partner, or Pencil Sharpener? Understanding Designers' Attitudes Towards Intelligent Creativity Support Tools
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
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.
Learning to See: You Are What You See
The exploration of these representations acts as a metaphor for the process of developing a visual understanding and/or visual vocabulary of the world.
Deep Meditations: Controlled navigation of latent space
We introduce a method which allows users to creatively explore and navigate the vast latent spaces of deep generative models.
Amplifying The Uncanny
Deep neural networks have become remarkably good at producing realistic deepfakes, images of people that (to the untrained eye) are indistinguishable from real images.
Searching for an (un)stable equilibrium: experiments in training generative models without data
This paper details a developing artistic practice around an ongoing series of works called (un)stable equilibrium.
Transforming the output of GANs by fine-tuning them with features from different datasets
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.
Calligraphic Stylisation Learning with a Physiologically Plausible Model of Movement and Recurrent Neural Networks
We propose a computational framework to learn stylisation patterns from example drawings or writings, and then generate new trajectories that possess similar stylistic qualities.
Real-time interactive sequence generation and control with Recurrent Neural Network ensembles
Recurrent Neural Networks (RNN), particularly Long Short Term Memory (LSTM) RNNs, are a popular and very successful method for learning and generating sequences.
Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks
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.
