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