论文标题
PARGAN：学习真正的参数性转换
ParGAN: Learning Real Parametrizable Transformations
论文作者
论文摘要
图像到图像翻译的当前方法产生了令人信服的结果，但是，由于现有机制通常受到限制且非直觉，因此很难控制应用的转换。我们提出了Pargan，这是对循环一致的GAN框架的概括，以通过简单而直观的控制来学习图像转换。所提出的生成器同时将图像和转换的参数化为输入。我们训练该网络以保留输入图像的内容，同时确保结果与给定参数化一致。我们的方法不需要配对数据，并且可以学习几个任务和数据集的转换。我们展示了如何在没有带注释的参数化的情况下进行脱节图像域，我们的框架可以创建平滑的插值以及同时学习多个转换。
Current methods for image-to-image translation produce compelling results, however, the applied transformation is difficult to control, since existing mechanisms are often limited and non-intuitive. We propose ParGAN, a generalization of the cycle-consistent GAN framework to learn image transformations with simple and intuitive controls. The proposed generator takes as input both an image and a parametrization of the transformation. We train this network to preserve the content of the input image while ensuring that the result is consistent with the given parametrization. Our approach does not require paired data and can learn transformations across several tasks and datasets. We show how, with disjoint image domains with no annotated parametrization, our framework can create smooth interpolations as well as learn multiple transformations simultaneously.
