Definition: U-Net, a CNN model designed for image recognition tasks, is specifically about combining multiple convolutional layers and pooling to improve efficiency and reduce computational resources required for training large models. It's a fundamental concept in computer vision, particularly for deep learning applications where high-resolution images are needed but time constraints limit the size of models. The key feature of U-Net is its ability to fuse different outputs from different layers, reducing computational costs by exploiting spatial locality between the output features from different scales. This architecture allows the network to learn complex representations that generalize well across different image domains.
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