U-Net

U-Net is a convolutional neural network that was developed for biomedical image segmentation at the Computer Science Department of the University of Freiburg.[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 GPU.

The U-Net architecture has also been employed in diffusion models for iterative image denoising.[3] This technology underlies many modern image generation models, such as DALL-E, Midjourney, and Stable Diffusion.

  1. ^ Ronneberger O, Fischer P, Brox T (2015). "U-Net: Convolutional Networks for Biomedical Image Segmentation". arXiv:1505.04597 [cs.CV].
  2. ^ Shelhamer E, Long J, Darrell T (Nov 2014). "Fully Convolutional Networks for Semantic Segmentation". IEEE Transactions on Pattern Analysis and Machine Intelligence. 39 (4): 640–651. arXiv:1411.4038. doi:10.1109/TPAMI.2016.2572683. PMID 27244717. S2CID 1629541.
  3. ^ Ho, Jonathan (2020). "Denoising Diffusion Probabilistic Models". arXiv:2006.11239 [cs.LG].