论文标题
图标：将照片转换为图标
Iconify: Converting Photographs into Icons
论文作者
论文摘要
在本文中，我们解决了照片和图像图像之间具有挑战性的域转换任务。尽管图标通常源自真实的对象图像（即照片），但应用严重的抽象和简化来生成专业图形设计师的图像图像。此外，两个域之间没有一对一的对应关系，因此，我们无法将其用作学习直接转换函数的基础真相。由于生成对抗网络（GAN）可以在没有任何信件的情况下解决域转换问题，因此我们测试CycleGAN和单元以从照片图像分段的对象中生成图标。我们使用多个图像数据集进行的实验证明，Cyclegan学习了足够的抽象和简化能力，可以生成图标样图像。
In this paper, we tackle a challenging domain conversion task between photo and icon images. Although icons often originate from real object images (i.e., photographs), severe abstractions and simplifications are applied to generate icon images by professional graphic designers. Moreover, there is no one-to-one correspondence between the two domains, for this reason we cannot use it as the ground-truth for learning a direct conversion function. Since generative adversarial networks (GAN) can undertake the problem of domain conversion without any correspondence, we test CycleGAN and UNIT to generate icons from objects segmented from photo images. Our experiments with several image datasets prove that CycleGAN learns sufficient abstraction and simplification ability to generate icon-like images.
