论文标题
钢铁网络：神经字体样式从重金属转移到公司徽标
Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos
论文作者
论文摘要
我们介绍了一种使用VGG16网络将样式从重金属乐队的徽标转移到公司徽标上的方法。我们确定了不同层和损失系数对风格学习，最小化手工艺品的贡献以及对公司徽标可读性的维护。我们发现层和损失系数在重金属风格和公司徽标可读性之间产生良好的权衡。这是使用生成网络迈向稀疏字体样式传输和公司徽标装饰的第一步。重金属和企业徽标在艺术上是非常不同的，他们强调情绪和可读性的方式，因此训练模型融合两者是一个有趣的问题。
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.
