Deep Learning

U-Net

An encoder–decoder architecture designed for precise image segmentation.

U-Net first compresses an image into high-level features and then expands those features back to the original resolution. Skip connections transfer fine spatial details from the encoder to matching decoder stages.

The design produces accurate pixel-level predictions and was originally developed for medical-image segmentation. U-Net variants are also widely used inside diffusion-based image-generation systems.

Also in Deep Learning

CNN RNN LSTM Transformers Auto Encoders Deep RL

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