U-Net is a kind of neural network originally purposed for medical image segmentation (~2015) given the name based on the u-shape form when drawn:
- In downsampling we operate on a feature extraction principle, applying 3x3 Convolutions followed by ReLu activation. Then iteratively apply a maxpool layer to reduce feature size while retaining features.
- On the bottleneck layer, where most of the important features have been extracted it is processed even further
- Then upsampling layer increases information using
- skip connections to regain spatial details that may have been lost when downsampling. we match a similar size region from downsampling, and copy a centered subset that matches width x height. Then we concatenate this to the channel dimension.
- deconvolution for increasing spatial size
