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.

U-NET Architecture Explained and Implementation

A quantization assisted U-Net study with ICA and deep features

Intuitive Explanation of Skip Connections in Deep Learning

Video restoration and visual quality enhancment using U-net

A Gentle Introduction to Generative Adversarial Networks (GANs

Modality specific U-Net variants for biomedical image segmentation

Video restoration and visual quality enhancment using U-net

U-Net (1).pptx

A Gentle Introduction to Pix2Pix Generative Adversarial Network

논문리뷰] A U-Net Based Discriminator for Generative Adversarial