Perceptual Adversarial Networks for Image-to-Image Transformation
Perceptual Adversarial Networks for Image-to-Image Transformation
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 …