具有表示学习和时间序列非线性关系的领域适应_.zip

# ODIN This repository contains the code used for the paper titled "Domain Adaptation with Representation Learning and Nonlinear Relation for Time Series" by Hussein A., Hajj H. [comming soon] ## High level approach description  ## Model  ## Requirements - python version ` 3.6.12 ` - To create anaconda environment run `conda env create -f environment.yml` ## Quick start 1. Download the PAR/HARR datasets - PAR: https://sensor.informatik.uni-mannheim.de/#dataset - HARR: http://archive.ics.uci.edu/ml/datasets/heterogeneity+activity+recognition#:~:text=The%20Heterogeneity%20Dataset%20for%20Human,%2C%20feature%20extraction%2C%20etc. 2. Run ```main.py``` in one of the following modes: - `cr_user`: cross user - `cr_device`: cross device - `cr_user_device`: cross user and cross device ``` python main.py --dataset PAR --mode cr_user --path "path/to/dataset" ``` ## Sample of reconstructed signals from test set after adaptation - Run ```inspect_AE.py``` to generate sample figures of advesarial examples   ## Domain adaptation toy example Toy examples for the limitations of domain adaptation with hard parameter sharing and how domain adaptation with soft parameter sharing overcomes these limitations [](https://colab.research.google.com/drive/1APQdDNW4zwRemgWM1mpTsjgb4-rcKVT9?usp=sharing)    ## Contacts - [Amir Hussein](https://github.com/AmirHussein96) anh21@mail.aub.edu ## Cite Paper: ``` @article{hussein2022domain, title={Domain Adaptation with Representation Learning and Nonlinear Relation for Time Series}, author={Hussein, Amir and Hajj, Hazem}, journal={ACM Transactions on Internet of Things}, volume={3}, number={2}, pages={1--26}, year={2022}, publisher={ACM New York, NY} } ```