A U-Net Based Discriminator for Generative Adversarial Networks, CVPR 2020 (10 min overview)

CVPR 2020

We improve the image synthesis quality of GANs by modifying the discriminator to take the shape of a popular segmentation network - U-Net, which allows to compute the adversarial loss on per-image and per-pixel basis. Consequently, the discriminator provides both global (image-level) and local (pixel-level) feedback to the generator and thereby enables the generator to produce higher quality images. The use of the segmentation-based discriminator also opens new possibilities for regularization to further improve image quality, which we demonstrate with our proposed CutMix-based consistency regularization. Link to the paper: https://arxiv.org/abs/2002.12655