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Found 6 papers, 1 papers with code
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.
Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities
The framework provides opportunities to hand over creative responsibilities to a generative system as targets for automation.
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.
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.
