The U-Net is an encoder-decoder neural network used for semantic segmentation. The implementation in this repository is a modified version of the U-Net proposed in this paper.
-
You can alter the U-Net's depth. The original U-Net uses a depth of 5, as depicted in the diagram above. The word "depth" specifically refers to the number of different spatially-sized convolutional outputs. With this U-Net implementation, you can easily vary the depth.
-
You can merge decoder and encoder pathways in two ways. In the original U-Net, the decoder and encoder activations are merged by concatenating channels. I've implemented a ResNet-style merging of the decoder and encoder activations by adding these activations. This was easy to code up, but it may not make sense theoretically and has not been tested.