Everyone can be an artist — deep learning for Neural Style Transfer and how to improve it.



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Conclusion

In this post, we have revisited the interpretation of Neural Style Transfer as aligning feature distributions in convolutional layers of a neural network. In this regard, existing methods only match first and second order moments. Contrarily, our method can be interpreted alternatively as minimizing an integral probability metric, or as matching all central moments up to a desired order and thus aligning the style distributions more faithfully.

If you want more details and theoretical backgrounds on existing approaches, make sure to check out our CVPR’21 paper and website at https://cmdnst.github.io/.

Kalischek, Nikolai, Jan D. Wegner, and Konrad Schindler. “In the light of feature distributions: moment matching for Neural Style Transfer.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.

References

[1] Leon A Gatys, Alexander S Ecker, and Matthias Bethge. Image style transfer using convolutional neural networks. In CVPR, 2016.
[2] Yanghao Li, Naiyan Wang, Jiaying Liu, and Xiaodi Hou. Demystifying neural style transfer. arXiv preprint arXiv:1701.01036, 2017.
[3] Xun Huang and Serge Belongie. Arbitrary style transfer in real-time with adaptive instance normalization. In ICCV,2017.
[4] Youssef Mroueh. Wasserstein style transfer. In AISTATS,2020.
[5] Werner Zellinger, Bernhard A Moser, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz. Robust unsupervised domain adaptation forneural networks via moment alignment. Information Sciences, 483:174–191, 2019.

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