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.
Understanding Pix2Pix GAN. The name itself says “Pixel to Pixel
Medical image segmentation using deep learning: A survey - Wang
Deep Learning Attention Mechanism in Medical Image Analysis
Can Generative Adversarial Networks help to overcome the limited
Understanding GAN Loss Functions
How diffusion models work: the math from scratch
Creating and training a U-Net model with PyTorch for 2D & 3D
U-GAN: Generative Adversarial Networks with U-Net for Retinal
Frontiers Improvement of Multiparametric MR Image Segmentation
U-Net Image Segmentation in Keras - PyImageSearch
Sensors, Free Full-Text
Must-Read Papers on GANs. Generative Adversarial Networks are one