- Prepare the running environment.
Packages used in our anaconda virtual environment are listed in 'environment.yml'.
- Prepare the data.
Download the office-home dataset. Generate the data list and replace the path 'xxxx' in 'data/office-home/*.txt' with your own path.
- Run our demo code.
The executive command for each domain adaptation (DA) method is shown in 'demo_home_ac.sh'. Using this script, you can reproduce our results of all these DA methods on the 'A to C' task of Office-home DA benchmark.
If you find this code useful for your research, please cite our paper
@article{hu2021adversarial,
title={Adversarial Domain Adaptation with Prototype-Based Normalized Output Conditioner},
author={Hu, Dapeng and Liang, Jian and Hou, Qibin and Yan, Hanshu and Chen, Yunpeng},
booktitle={IEEE Transactions on Image Processing (TIP)},
pages={9359--9371},
volume={30},
year={2021},
publisher={IEEE} }