U-Net GAN Explained
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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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The Complete Guide to Generative Adversarial Networks [GANs]
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A quantization assisted U-Net study with ICA and deep features
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Image Segmentation with U-Net in PyTorch: The Grand Finale of the
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How U-net works? ArcGIS API for Python
Difference between PatchGAN and Fully Convolutional Layer
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How to Implement Pix2Pix GAN Models From Scratch With Keras
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From DALL·E to Stable Diffusion: How Do Text-to-Image Generation
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U-GAN: Generative Adversarial Networks with U-Net for Retinal
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Generative Adversarial Networks: Create Data from Noise
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Modality specific U-Net variants for biomedical image segmentation
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U-Net: Convolutional Networks for Biomedical Image Segmentation