# Post-Train Adaptive U-Net for Image Segmentation

arXiv: https://arxiv.org/abs/2301.06358 · HTML: https://mlsanity.com/paper/2301.06358/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 17 references that are in the map.
- First posted on arXiv 16 Jan 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Adaptive Computation Time for Recurrent Neural Networks](https://mlsanity.com/paper/1603.08983/) (2016)
- [Spatially Adaptive Computation Time for Residual Networks](https://mlsanity.com/paper/1612.02297/) (2016)
- [Fast Facial Landmark Detection and Applications: A Survey](https://mlsanity.com/paper/2101.10808/) (2021)
- [Face Detection on Mobile: Five Implementations and Analysis](https://mlsanity.com/paper/2205.05572/) (2022)
- [Faster Optimization-Based Meta-Learning Adaptation Phase](https://mlsanity.com/paper/2206.05930/) (2022)
- [Post-Train Adaptive MobileNet for Fast Anti-Spoofing](https://mlsanity.com/paper/2207.13410/) (2022)

## Abstract (arXiv)

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.