artistic research project, generative artwork, installation, essay

TroublingGAN is a critical generative artwork and artistic research project that experiments with the StyleGAN vector-image generative neural network. It experiments with alternative use of GANs as a tool for artistic research.


TroublingGAN is a custom-trained StyleGAN︎︎︎ model that generates a visually ambiguous images depicting "troubling times". It is able to derive an essence from a dataset representing a "troubling times" - and project the affective quality of photojournalism from the dataset onto the generated outputs. The motivation for creating this seemingly imperfect generative model was to arrive at a new type of understanding of the vague notion of "troubling times" using a neural network perspective.


The project uncovered a number of issues related to visual representations of disturbing events and disasters and the affective value of contemporary photojournalism. Such journalistic photography is being often re-used in different contexts, which causes troubling unethical use of sensitive visual material as illustrative stock photos. This project argues against recontextualization of journalistic photography and suggests speculative use of visually ambiguous generated imagery instead. Replacing the once-photojournalistic-new-stock-photo with generated semi-abstract visuals coming from a neural network that has learned from similar thematically identical photographs, this ethical problem ceases to exist and space is created for a different way of perceiving the image.


Such images do not burden the viewer with unnecessary meanings and the frequent information noise does not arise. They are devoid of context, but still carry the necessary atmosphere. But despite the absence of specific objects and scenes, these visually ambiguous images are still strangely reminiscent of photography. This is very confusing to the human eye. The mind is constantly trying to assign some meaning to these obscure compositions, however abstract. But the assigned meaning or interpretation becomes dynamic and constantly changing, and therefore it is ultimately the atmosphere and emotional charge that affects the viewer. Thus, neural networks, through their interpretation of the dataset, offer a different understanding of the affective quality of journalistic photographs.

photos from the exhibition 

TroublingGAN was exhibited in a fallout shelter as part of ELBE DOCK film festival programme:  “Behind open eyes” 18.5.-22.5.2022, Ústí nad Labem, CZ

Nam sed tortor eget diam blandit
Fringilla a sit amet

(datset video)

Exhibition included also presentation of the trainig dataset and a reading station with essay describing the background of the project

Read a longer description of TroublingGAN project: https://troublinggan.hamosova.com/essay.html


Read an essay written for AIxDesign: https://medium.com/aixdesign/troublinggan-fa98eea2e37f

Generate your own TroublingGAN images: https://huggingface.co/spaces/lenkahamosova/troublinggan

A journal article TroublingGAN: generated visual ambiguity as a speculative alternative to photojournalism (currently in peer-review process in Journal of Artistic Research)

TroublingGAN (2021-22)

https://troublinggan.hamosova.com/

TroublingGAN demo:
https://huggingface.co/spaces/lenkahamosova/troublinggan

Concept and research: Lenka Hamosova

Technical guidance: Pavol Rusnak

Scraping magician: Adriana Homolova

Original code: StyleGAN2 (Karras et al.)

FAMU Prague, 2021

This work was created at the Academy of Performing Arts in Prague as part of the project "Extending the creative tools of Machine Learning and Artificial Intelligence - Experimental Tools in Artistic Practice" supported by the Ministry of Education and Science for specific university research at the Academy of Performing Arts in Prague in 2021.