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.

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

Contacts

• Amir Hussein 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}
}