桥梁之间的分层多任务无监督域适应用于路过损伤诊断

# HierMUD: a Hierarchical Multi-task Unsupervised Domain adaptation framework This is the repository for the paper: >* Jingxiao Liu, Susu Xu, Mario Bergés, Hae Young Noh. HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis. [[slides]](docs/slides.pdf)[[paper]](https://arxiv.org/abs/2107.11435)[[video]](docs/video.mp4) ### Description We introduce HierMUD, a novel approach for multi-task unsupervised domain adaptation. This approach is developed for bridge health monitoring using drive-by vehicle vibrations, but it can be applied to other problems, such as digit recognition, image classification, etc.  In this repository, we demonstrate our approach through two examples: - A drive-by bridge health monitoring example, which transfers model learned using vehicle vibration data collected from one bridge to detect, localize and quantify damage on another bridge. - A digit recognition example, which transfers model learned using MNIST data to MNIST-M data and conducts two tasks: odd-even classification and digits comparison. Note: the drive-by bridge health monitoring experiment involves data that is not publicly available. We will work towards making the experiment replicable without violating data usage policy. ### Code Usage ``` git clone https://github.com/jingxiaoliu/HierMUD.git cd HierMUD ``` - Run the drive-by bridge health monitoring example with ``` jupyter notebook demo_dbbhm.ipynb ``` - Run the digit recognition example with ``` jupyter notebook demo_mnist.ipynb ``` ### Contact Feel free to send any questions to: - [Jingxiao Liu](mailto:[email protected]), Ph.D. Candidate at Stanford University, Department of Civil and Environmental Engineering. ### Citation If you use this implementation, please cite our paper as follows: ``` @misc{liu2021hiermud, title={HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis}, author={Jingxiao Liu and Susu Xu and Mario Bergés and Hae Young Noh}, year={2021}, eprint={2107.11435}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```