From 18163ce450f99de24d0f1d907f521f97c0ac9b07 Mon Sep 17 00:00:00 2001 From: csteindl Date: Tue, 9 Jun 2020 09:23:35 +0000 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c6d6cfe..3b7aa58 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,3 @@ # ONB Postcards StyleGAN -During the [ONB Labs Symposium 2019](https://labs.onb.ac.at/en/topic/labs-symposium-2019/) Gene Kogan and Sofia Crespo held a workshop helping artists expand their knowledge of technology and helping tech people learn to be more artistic and creative. Wherever participants were on that spectrum, they were invited you to join this workshop on AI art using the ONB Labs data. The course introduced a family of machine learning-based techniques which synthesise, transfer, collage, and remix the styles of images. One of these techniques are Generative Adversarial Networks (GANs), in which two adverse networks operate, one of which is generating results while the other compares it to a set of "real" data. The goal of the generating network is to create results that are indistinguishable from the underlying data. After the ONB Labs event Gene and Sofia trained a StyleGAN based on our historical postcard set. The result presented here in the form of a video is – to say it with the creators words – surprisingly nice. \ No newline at end of file +During the [ONB Labs Symposium 2019](https://labs.onb.ac.at/en/topic/labs-symposium-2019/) [Gene Kogan](https://genekogan.com/) and [Sofia Crespo](https://sofiacrespo.com/) held a workshop helping artists expand their knowledge of technology and helping tech people learn to be more artistic and creative. Wherever participants were on that spectrum, they were invited you to join this workshop on AI art using the ONB Labs data. The course introduced a family of machine learning-based techniques which synthesise, transfer, collage, and remix the styles of images. One of these techniques are Generative Adversarial Networks (GANs), in which two adverse networks operate, one of which is generating results while the other compares it to a set of "real" data. The goal of the generating network is to create results that are indistinguishable from the underlying data. After the ONB Labs event Gene and Sofia trained a StyleGAN based on our historical postcard set. The result presented here in the form of a video is – to say it with the creators words – surprisingly nice. \ No newline at end of file -- GitLab