Abstract
In this paper, we propose a principled Perceptual Adversarial Networks (PAN) for image-to-image transformation tasks. Unlike existing application-specific algorithms, PAN provides a generic framework of learning mapping relationship between paired images (Fig. 1), such as mapping a rainy image to its de-rained counterpart, object edges to its photo, semantic labels to a scenes image, etc. The proposed PAN consists of two feed-forward convolutional neural networks (CNNs), the image transformation network T and the discriminative network D. Through combining the generative adversarial loss and the proposed perceptual adversarial loss, these two networks can be trained alternately to solve image-to-image transformation tasks. Among them, the hidden layers and output of the discriminative network D are upgraded to continually and automatically discover the discrepancy between the transformed image and the corresponding ground-truth. Simultaneously, the image transformation network T is trained to minimize the discrepancy explored by the discriminative network D. Through the adversarial training process, the image transformation network T will continually narrow the gap between transformed images and ground-truth images. Experiments evaluated on several image-to-image transformation tasks (e.g., image de-raining, image inpainting, etc.) show that the proposed PAN outperforms many related state-of-the-art methods.
Abstract (translated)
在本文中，我们提出了一个原则性的知觉对抗网络（PAN），用于图像到图像的转换任务。与现有的特定于应用的算法不同，PAN提供了一个学习成对图像之间映射关系的通用框架（图1），例如将雨点图像映射到其去雨点对应物、将对象边缘映射到其照片、将语义标签映射到场景图像等。所提出的PAN由两个前馈卷积神经网络组成。（cnns）、图像转换网络t和识别网络d，通过将生成性对抗损失和提出的感知性对抗损失相结合，对这两个网络进行交替训练，以解决图像到图像的转换任务。其中，对识别网络D的隐藏层和输出进行了升级，以不断自动地发现变换后的图像与相应的地面真值之间的差异。同时，对图像变换网络T进行训练，使识别网络D所发现的差异最小化，通过对抗训练过程，图像变换网络T将不断缩小变换后的图像与地面真值图像之间的距离。对多个图像到图像转换任务（如图像去雨、图像修复等）进行的实验评估表明，所提出的PAN优于许多相关的最新方法。
URL
https://arxiv.org/abs/1706.09138
