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In the light of feature distributions: Moment matching for Neural Style Transfer (CVPR 2021)
This repository provides code to recreate results presented in In the light of feature distributions: Moment matching for Neural Style Transfer.
For more information, please see the project website and make sure to check out our medium blog post here
<hr /> <img src="assets/teaser.jpg" />Contact
If you have any questions, please let me <a href='mailto:niko%6Cai%2E%6Ba%6Ci%73c%68ek@ge%6Fd.bau%67.eth%7A.%63h'>know</a>
Instructions
Running neural style transfer with Central Moment Discrepancy is as easy as running
python main.py --c_img ./path/to/content.jpg --s_img ./path/to/style.jpg
You have the following command line arguments to change to your needs:
<pre> --c_img The content image that is being stylized. --s_img The style image --epsilon Iterative optimization is stopped if delta value of moving average loss is smaller than this value. --max_iter Maximum iterations if epsilon is not surpassed --alpha Convex interpolation of style and content loss (should be set high > 0.9 since we start with content as target) --lr Learning rate of Adam optimizer --im_size Output image size. Can either be single integer for keeping aspect ratio or tuple. </pre>Citations
@article{kalischek2021light,
title={In the light of feature distributions: moment matching for Neural Style Transfer},
author={Nikolai Kalischek and Jan Dirk Wegner and Konrad Schindler},
year={2021},
eprint={2103.07208},
archivePrefix={arXiv},
primaryClass={cs.CV}
}