  Among the major remaining challenges for generative adversarial networks
(GANs) is the capacity to synthesize globally and locally coherent images with
object shapes and textures indistinguishable from real images. To target this
issue we propose an alternative U-Net based discriminator architecture,
borrowing the insights from the segmentation literature. The proposed U-Net
based architecture allows to provide detailed per-pixel feedback to the
generator while maintaining the global coherence of synthesized images, by
providing the global image feedback as well. Empowered by the per-pixel
response of the discriminator, we further propose a per-pixel consistency
regularization technique based on the CutMix data augmentation, encouraging the
U-Net discriminator to focus more on semantic and structural changes between
real and fake images. This improves the U-Net discriminator training, further
enhancing the quality of generated samples. The novel discriminator improves
over the state of the art in terms of the standard distribution and image
quality metrics, enabling the generator to synthesize images with varying
structure, appearance and levels of detail, maintaining global and local
realism. Compared to the BigGAN baseline, we achieve an average improvement of
2.7 FID points across FFHQ, CelebA, and the newly introduced COCO-Animals
dataset. The code is available at https://github.com/boschresearch/unetgan.
