The U-Net uses an upsampling mechanism and has a fully-connected layer.

U-Net is a convolutional neural network architecture designed for biomedical image segmentation. It consists of a contracting path for feature extraction and an expansive path for precise localization, usingupsamplingin the decoding path. However, U-Netdoes not include fully-connected layers; instead, it uses only convolutional layers to maintain spatial information. Removing fully-connected layers ensures the network can handle images of varying sizes without requiring fixed input dimensions.

Exact Extract from HCIP-AI EI Developer V2.5:

"U-Net architecture is fully convolutional and avoids fully-connected layers to preserve spatial resolution, relying on upsampling in the decoder path for segmentation tasks."