Adapting DenseNet for semantic segmentation
DenseNet is very suitable for semantic segmentation because of its efficiency, accuracy, and abundance of skip layers. In fact, using DenseNet for semantic segmentation proves to be effective even when the dataset is limited and when a label is underrepresented.
To use DenseNet for semantic segmentation, we need to be able to build the right side of the U network, which means that we need the following:
- A way to increase the resolution; if we call the transition layers of DenseNet transition down, then we need transition-up layers.
- We need to build the skip layers to join the left and right side of the U network.
Our reference network is FC-DenseNet, also known as one hundred layers tiramisu, but we are not trying to reach 100 layers.
In practice, we want to achieve an architecture similar to the following:
Figure 9.8 – Example of FC-DenseNet architecture
The horizontal red arrows...
