Rue Saint Ursule, Montpellier, France (2000)
All links of one day
in a single page.
<Previous day - Next day>

rss_feedDaily RSS Feed
floral_left The Daily Shaarli floral_right
——————————— Sunday 16 September 2018 ———————————
ai - generator - image - remix - software - video -

Repaint your picture in the style of your favorite artist.

About

Our mission is to provide a novel artistic painting tool that allows everyone to create and share artistic pictures with just a few clicks. All you need to do is upload a photo and choose your favorite style. Our servers will then render your artwork for you. We apply an algorithm developed by Leon Gatys, Alexander Ecker and Matthias Bethge. The website was originally created by Łukasz Kidziński and Michał Warchoł. We have now joined forces to provide you with the latest technology in even more accessible way.

Our Team

Five researchers from the Bethge lab at University of Tübingen (Germany), CHILI Lab at École polytechnique fédérale de Lausanne (Switzerland) and Université catholique de Louvain (Belgium).

ai - generative - image - remix - software -
It's all a game of construction — some with a brush, some with a shovel, some choose a pen.
Jackson Pollock

…and some, including myself, choose neural networks. I’m an artist, and I've also been building commercial software for a long while. But art and software used to be two parallel tracks in my life; save for the occasional foray into generative art with Processing and computational photography, all my art was analog… until I discovered GANs (Generative Adversarial Networks).

Since the invention of GANs in 2014, the machine learning community has produced a number of deep, technical pieces about the technique (such as this one). This is not one of those pieces. Instead, I want to share in broad strokes some reasons why GANs are excellent artistic tools and the methods I have developed for creating my GAN-augmented art.

https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/

-