SPatchGAN: A Statistical Feature Based Discriminator for Unsupervised Image-to-Image Translation
This work addresses image-to-image translation for computer vision applications, offering a simplified framework that enhances shape deformation and fine details.
The authors tackled the problem of unsupervised image-to-image translation by proposing a discriminator that focuses on statistical features rather than individual patches, resulting in improved performance in applications like selfie-to-anime, male-to-female, and glasses removal.
For unsupervised image-to-image translation, we propose a discriminator architecture which focuses on the statistical features instead of individual patches. The network is stabilized by distribution matching of key statistical features at multiple scales. Unlike the existing methods which impose more and more constraints on the generator, our method facilitates the shape deformation and enhances the fine details with a greatly simplified framework. We show that the proposed method outperforms the existing state-of-the-art models in various challenging applications including selfie-to-anime, male-to-female and glasses removal.