decoy

NORBERT SCHOERNER

FITZROVIA CHAPEL
Fitzroy Place
2 Pearson Square
London
W1T 3BF

29th September to 2nd October 2021

 

With thanks
Daniel Cheetham, Rainer Usselmann at Happy Finish
Coding by Marco Marchesi

Dates

Exhibition Dates
29th September – 2nd October 2021

Fitzrovia Chapel
Fitzroy Place
2 Pearson Square
London, W1T 3BF

Private View – Wednesday 29th 6 – 9pm
Thursday 30th 12 – 8pm
Friday 1st 12 – 7pm
Saturday 2nd 11 – 6pm

Artists

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

Information

decoy brings together 14 works by German photographer, artist and visualiser Norbert Schoerner created between 2018-19 with the aid of a Generative Adversarial Network (GAN). The GAN, a machine learning framework, assists in realising scenes, initially imagined by Schoerner in the form of brief textual vignettes, as digital .PNG files. The resulting prints are eerie, unfamiliar interpretations pulled from unknown fields of information.

These abstractions took root in Schoerner’s project Pictures I Never Took (2011 – 2017): decoy builds on from this previous investigation into text and image, creation and perception, by challenging received notions of consciousness, creativity and collaboration.

“Worlds presented by a new imagination at work, an intelligence other than our own, they are flowers of unknowable romance pulled from unfamiliar fields of information”. 

 

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