U-Net GAN Explained

In contrast to typical GANs, a U-Net GAN uses a segmentation network as the discriminator. This segmentation network predicts two classes: real and fake. In doing so, the discriminator gives the generator region-specific feedback. This discriminator design also enables a CutMix-based consistency regularization on the two-dimensional output of the U-Net GAN discriminator, which further improves image synthesis quality.

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Understanding GAN Loss Functions

Attention UNET and its Implementation in TensorFlow - Idiot Developer

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42: A blog on A.I.

Modality specific U-Net variants for biomedical image segmentation