Perceptual-DualGAN: Perceptual Losses for Image to Image Translation with Generative Adversarial Nets

Thinking about cross-domain image-to-image translation problems, where an input image belonging to domain U is transformed into an output image belonging to another domain V. A series of typical tasks, such as style transformation, colorization, super-resolution, can be seen as cross-domain image-to-image translation tasks. Recent methods such as Conditional Generative Adversarial Networks (cGANs) make big progress in this field, but they require paired image data, which is hard to obtain. The DualGAN (Unsupervised Dual Learning for Image-to-Image Translation) architecture was proposed to solve the issue of lack of paired data. But the pixel-level reconstruction losses of DualGAN are simple. In this paper, we replace the pixel-level reconstruction losses with the perceptual reconstruction losses, and propose a more advanced framework for cross-domain image-to-image translation named perceptual-DualGAN. The perceptual reconstruction losses consist of feature reconstruction losses and style reconstruction losses, both of them are computed from pretrained loss networks. Experiments on multiple image translation tasks show that our framework almost performs superior to other methods. And the results of experiments illustrate that our framework can generate more realistic and more natural photos.