论文标题
想象力：使用神经风格转移的重新贴上应用
ImagineNet: Restyling Apps Using Neural Style Transfer
论文作者
论文摘要
本文介绍了ImaginEnet，该工具使用新型的神经风格转移模型使最终用户和应用程序开发人员可以使用其选择的图像重新设计GUIS。以前的神经风格转移技术对于此应用而言不足，因为它们产生的GUI是难以辨认的，因此是非功能的。我们通过在原始配方中添加一个新的损失项来提出一个神经解决方案，从而最大程度地减少了样式和输出图像之间CNN中不同级别的特征的毫无序列的误差。 ImaginEnet保留了Guis的细节，同时转移了艺术的颜色和纹理。我们向50名评估者展示了Imaginenet以及其他样式转移技术的GUIS，并且都更喜欢ImaginEnet的评估者。我们展示了如何使用ImaginEnet来重述（1）应用程序的图形资产，（2）具有用户提供内容的应用程序，以及（3）具有动态生成GUIS的应用程序。
This paper presents ImagineNet, a tool that uses a novel neural style transfer model to enable end-users and app developers to restyle GUIs using an image of their choice. Former neural style transfer techniques are inadequate for this application because they produce GUIs that are illegible and hence nonfunctional. We propose a neural solution by adding a new loss term to the original formulation, which minimizes the squared error in the uncentered cross-covariance of features from different levels in a CNN between the style and output images. ImagineNet retains the details of GUIs, while transferring the colors and textures of the art. We presented GUIs restyled with ImagineNet as well as other style transfer techniques to 50 evaluators and all preferred those of ImagineNet. We show how ImagineNet can be used to restyle (1) the graphical assets of an app, (2) an app with user-supplied content, and (3) an app with dynamically generated GUIs.
