# Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos

arXiv: https://arxiv.org/abs/2001.03659 · HTML: https://mlsanity.com/paper/2001.03659/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Font Style that Fits an Image -- Font Generation Based on Image Context](https://mlsanity.com/paper/2105.08879/) (2021)
- [Logo Generation Using Regional Features: A Faster R-CNN Approach to Generative Adversarial Networks](https://mlsanity.com/paper/2109.12628/) (2021)

## Abstract (arXiv)

We introduce a method for transferring style from the logos of heavy metal bands onto corporate logos using a VGG16 network. We establish the contribution of different layers and loss coefficients to the learning of style, minimization of artefacts and maintenance of readability of corporate logos. We find layers and loss coefficients that produce a good tradeoff between heavy metal style and corporate logo readability. This is the first step both towards sparse font style transfer and corporate logo decoration using generative networks. Heavy metal and corporate logos are very different artistically, in the way they emphasize emotions and readability, therefore training a model to fuse the two is an interesting problem.