Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos
This is an incremental step for sparse font style transfer and corporate logo decoration, targeting designers and branding professionals.
The paper tackled the problem of transferring heavy metal band logo styles onto corporate logos using a VGG16 network, finding specific layers and loss coefficients that balance style and readability.
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.