  Typical neural network architectures used for image segmentation cannot be
changed without further training. This is quite limiting as the network might
not only be executed on a powerful server, but also on a mobile or edge device.
Adaptive neural networks offer a solution to the problem by allowing certain
adaptivity after the training process is complete. In this work for the first
time, we apply Post-Train Adaptive (PTA) approach to the task of image
segmentation. We introduce U-Net+PTA neural network, which can be trained once,
and then adapted to different device performance categories. The two key
components of the approach are PTA blocks and PTA-sampling training strategy.
The post-train configuration can be done at runtime on any inference device
including mobile. Also, the PTA approach has allowed to improve image
segmentation Dice score on the CamVid dataset. The final trained model can be
switched at runtime between 6 PTA configurations, which differ by inference
time and quality. Importantly, all of the configurations have better quality
than the original U-Net (No PTA) model.
