Methods › Computer Vision › Semantic Segmentation Models › U-Net

U-Net

2,588 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

U-Net is an architecture for semantic segmentation. It consists of a contracting path and an expansive path. The contracting path follows the typical architecture of a convolutional network. It consists of the repeated application of two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit (ReLU) and a 2x2 max pooling operation with stride 2 for downsampling. At each downsampling step we double the number of feature channels. Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 convolution (“up-convolution”) that halves the number of feature channels, a concatenation with the correspondingly cropped feature map from the contracting path, and two 3x3 convolutions, each followed by a ReLU. The cropping is necessary due to the loss of border pixels in every convolution. At the final layer a 1x1 convolution is used to map each 64-component feature vector to the desired number of classes. In total the network has 23 convolutional layers.

Original MATLAB Code

Source: U-Net: Convolutional Networks for Biomedical Image SegmentationSee Code · milesial/Pytorch-UNet

Papers archive 2025-07-28

30 shown of 2,588, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 768 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Segmentation855
Semantic Segmentation659
Image Segmentation450
Medical Image Segmentation263
Decoder248
Denoising171
Deep Learning144
Image Generation123
Tumor Segmentation122
Data Augmentation101
Computed Tomography (CT)81
Diagnostic81
Lesion Segmentation77
Medical Image Analysis77
Transfer Learning72
Brain Tumor Segmentation64
Super-Resolution64
SSIM61
GPU60
Generative Adversarial Network51

Usage over time archive 2025-07-28

Papers per year tagged with U-Net: 2015 to 2025, peak 523 523 0 2015: 1 paper 2015 2016: 1 paper 2016 2017: 35 papers 2017 2018: 126 papers 2018 2019: 225 papers 2019 2020: 332 papers 2020 2021: 343 papers 2021 2022: 345 papers 2022 2023: 420 papers 2023 2024: 523 papers 2024 2025: 237 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (2,588 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Semantic Segmentation Models

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