  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.
