U-Net - Wikipedia
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 …
U-Net Architecture Explained - GeeksforGeeks
Oct 9, 2025 · U-Net is a kind of neural network mainly used for image segmentation which means dividing an image into different parts to identify specific objects for example separating a tumor from …
[1505.04597] U-Net: Convolutional Networks for Biomedical ...
May 18, 2015 · View a PDF of the paper titled U-Net: Convolutional Networks for Biomedical Image Segmentation, by Olaf Ronneberger and Philipp Fischer and Thomas Brox
U-Net for Beginners: A Step-by-Step Guide
Jun 14, 2025 · Get started with U-Net in machine learning. Understand its basics, learn how to implement it, and explore its applications.
Understanding U-Net Architecture in Deep Learning
May 29, 2025 · U-Net is a convolutional neural network (CNN) architecture developed by Olaf Ronneberger et al. in 2015, designed for semantic segmentation (pixel classification).
Image Segmentation with U-Net Explained Simply - ML Journey
Sep 23, 2025 · U-Net gets its name from its distinctive U-shaped architecture, which consists of two main paths: a contracting path (encoder) and an expanding path (decoder). This design was …
Understanding U-Net Architecture in Deep Learning
May 28, 2025 · U-Net is a powerful deep learning architecture designed for semantic segmentation, especially in medical imaging. This guide breaks down its structure, working, implementation, …