U-Net is a convolutional neural network architecture primarily used for image segmentation. It was developed for biomedical image analysis but has found applications in various fields. Its U-shape is characterized by a contracting path (encoder) for feature extraction and an expansive path (decoder) for precise localization. Skip connections between corresponding layers in the encoder and decoder help preserve fine-grained details, enabling accurate pixel-level classification.
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