Learning to See: You Are What You See
Computer Vision and Pattern Recognition
2020-03-03 v1 Graphics
Human-Computer Interaction
Machine Learning
Abstract
The authors present a visual instrument developed as part of the creation of the artwork Learning to See. The artwork explores bias in artificial neural networks and provides mechanisms for the manipulation of specifically trained for real-world representations. The exploration of these representations acts as a metaphor for the process of developing a visual understanding and/or visual vocabulary of the world. These representations can be explored and manipulated in real time, and have been produced in such a way so as to reflect specific creative perspectives that call into question the relationship between how both artificial neural networks and humans may construct meaning.
Cite
@article{arxiv.2003.00902,
title = {Learning to See: You Are What You See},
author = {Memo Akten and Rebecca Fiebrink and Mick Grierson},
journal= {arXiv preprint arXiv:2003.00902},
year = {2020}
}
Comments
Presented as an Art Paper at SIGGRAPH 2019