128x128 pixel Samples from CAN train on WikiART.
A WIP implementation of CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms. Repo bases DCGAN implementation on DCGAN-tensorflow with modifications to reduce checkerboard artifacts according to this distill article
The paper authors basically modified the GAN objective to encourage the network to deviate away from art norms.
We used this compiled wikiart dataset available here. Using the dataset is subject to wikiart's terms of use
Extract the dataset, then set the path in train.sh
Edit the parameters of train.sh then
bash train.sh
If you use this implementation in your own work please cite the following
@misc{2017cans,
author = {Phillip Kravtsov and Phillip Kuznetsov},
title = {Creative Adversarial Networks},
year = {2017},
howpublished = {\url{https://github.com/mlberkeley/Creative-Adversarial-Networks}},
note = {commit xxxxxxx}
}