U-Net

U-Net is a convolutional neural network that was developed for image segmentation.^[1] The network is based on a fully convolutional neural network^[2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation. Segmentation of a 512 × 512 image takes less than a second on a modern (2015) GPU using the U-Net architecture.^{[1] [3][4][5]}

The U-Net architecture has also been employed in diffusion models for iterative image denoising.^[6] This technology underlies many modern image generation models, such as DALL-E, Midjourney, and Stable Diffusion.