How does the U-NET architecture leverage skip connections to enhance the precision and detail of semantic segmentation outputs, and why are these connections important for backpropagation?

The U-NET architecture, introduced by Ronneberger et al. in 2015, is a convolutional neural network (CNN) designed for biomedical image segmentation. Its structure is characterized by a symmetric U-shaped architecture, which includes an encoder-decoder structure with skip connections that play a important role in enhancing the precision and detail of semantic segmentation outputs. These skip

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