论文标题
面部属性反转
Face Attribute Invertion
论文作者
论文摘要
在两个领域之间操纵人的面部图像是一个重要而有趣的问题。大多数现有方法通过应用两个具有额外条件输入的发电机或一个发电机来解决此问题。在本文中，我们提出了一种基于gan的新型自我感知方法，用于自动面部属性逆。所提出的方法将面部图像作为输入，仅采用一个单个发电机，而无需在其他输入上进行调节。我们的模型从多损失策略和修改后的U-NET结构中获利，在训练方面非常稳定，并且能够保留原始面部图像的细节。
Manipulating human facial images between two domains is an important and interesting problem. Most of the existing methods address this issue by applying two generators or one generator with extra conditional inputs. In this paper, we proposed a novel self-perception method based on GANs for automatical face attribute inverse. The proposed method takes face images as inputs and employs only one single generator without being conditioned on other inputs. Profiting from the multi-loss strategy and modified U-net structure, our model is quite stable in training and capable of preserving finer details of the original face images.
