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We introduce a method for transferring style from the logos of heavy metalbands onto corporate logos using a VGG16 network. We establish the contributionof different layers and loss coefficients to the learning of style,minimization of artefacts and maintenance of readability of corporate logos. Wefind layers and loss coefficients that produce a good tradeoff between heavymetal style and corporate logo readability. This is the first step both towardssparse font style transfer and corporate logo decoration using generativenetworks. Heavy metal and corporate logos are very different artistically, inthe way they emphasize emotions and readability, therefore training a model tofuse the two is an interesting problem.

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