Artist’s Note

The ability to connect with an artwork involves attention to detail, our aesthetic sensibilities and is deeply personal. However, in this age of sensory and visual overload we have to ask whether we’re ever in front of an image for long enough?

I believe that the growing habit of skimming directly relates to our struggle to hold certain encounters in our long-term memories. This inability to hold on to facts and experiences, I want to suggest, limits the extent of our imagination and can be a challenge to our emotional wellbeing.

Conceived as a two-part project, the first part consists of a series of plates, reminiscent of Ophthalmologists’ charts. They describe photographic images and encounters, rather than depict them. The second part of the project then sees the feeding of the textual plates into a GAN based machine-learning system. The ‘Story-to-Image’ Generator is an AI system that is able to visualise what is described in a text. The neural networks then conceive unique pictures based on the image descriptions by using ML image datasets.

Investigating divergent levels of abstraction, we based the project on the AttnGAN approach, implemented in PyTorch machine learning framework. In particular, AttnGAN uses an attentional generative network that synthesises progressively refined details in different regions of the image. It does so by observing the structure of the prose and the relevant word