HDA implemented in PyTorch
code release for "Heuristic Domain Adaptation"(NIPS2020)
One sentence highlight
We address the construction of domain-invariant and domain-specific representations from the heuristic search perspective.
Poster
Enviroment
- pytorch = 1.3.0
- torchvision = 0.4.1
- numpy = 1.17.2
- pillow = 6.2.0
- python3.7
- cuda10
To install the required python packages, run
pip install -r requirements.txt
Dataset
Office-Home dataset can be found here.
Domainnet dataset can be found here.
The training of HDA could be utilized by changing the path of the dataset, such as the txt files in data/UDA_officehome/Art.txt.
Also, the txt files for SSDA and MSDA should be compressed.
cd data
unzip data.zip
Train
UDA on Office-Home
bash scripts/run_uda.sh
MSDA on Domainnet
bash scripts/run_msda.sh
SSDA on Domainnet
bash scripts/run_ssda.sh
Citation
If you use this code for your research, please consider citing:
@inproceedings{cui2020hda,
title={Heuristic Domain Adaptation},
author={Cui, Shuhao and Jin, Xuan, and Wang, Shuhui and He, Yuan and Huang, Qingming},
booktitle={Advances in Neural Information Processing Systems},
year={2020}
}
Contact
If you have any problem about our code, feel free to contact
or describe your problem in Issues.