Abstract
This paper introduces a neural style transfer model to conditionally generate a stylized image using only a set of examples describing the desired style. The proposed solution produces high-quality images even in the zero-shot setting and allows for greater freedom in changing the content geometry. This is thanks to the introduction of a novel Peer-Regularization Layer that recomposes style in latent space by means of a custom graph convolutional layer aiming at separating style and content. Contrary to the vast majority of existing solutions our model does not require any pre-trained network for computing perceptual losses and can be trained fully end-to-end with a new set of cyclic losses that operate directly in latent space. An extensive ablation study confirms the usefulness of the proposed losses and of the Peer-Regularization Layer, with qualitative results that are competitive with respect to the current state-of-the-art even in the challenging zero-shot setting. This opens the door to more abstract and artistic neural image generation scenarios and easier deployment of the model in. production
Abstract (translated)
本文介绍了一种神经风格转换模型，仅通过一组描述所需风格的例子，有条件地生成一个风格化的图像。所提出的解决方案即使在零镜头设置下也能生成高质量的图像，并允许更自由地更改内容几何图形。这是由于引入了一种新的对等正则化层，该层通过一个定制的图形卷积层来重新编译潜在空间中的样式，目的是分离样式和内容。与现有的绝大多数解决方案相反，我们的模型不需要任何经过预先培训的网络来计算感知损失，并且可以通过直接在潜在空间中运行的一组新的循环损失进行端到端的全面培训。一项广泛的烧蚀研究证实了所提议的损失和对等正则化层的有用性，定性结果与当前最先进的技术相竞争，即使在具有挑战性的零触发设置中也是如此。这为更抽象和艺术的神经图像生成场景和更容易在中部署模型打开了大门。生产
URL
https://arxiv.org/abs/1906.02913
