DEEM

The codes for "context-guided entropy minimization for semi-supervised domain adaptation". The work can be downloaded from here.

Env setting

Python=3.8
Pytorh=1.7.0 (py3.8_cuda11.0.221)
torchvision=0.8.1  

Dataset setting

1. All datasets can be seen in Office-31, office-home, multi (DomainNet126). After these dataset are downloaded, please bulid a new folder named ./data/ and put the dataset into it. An example is like:

   • DEEM-master/data

     • Office-31

       • webcam

         • images
           • back_pack
             • frame_0001.jpg
             • frame_0002.jpg
             • frame_0003.jpg

2. To specify your dataset path, please set "project_root" in return_dataset.py

Training usages

Training on Office-31 on 1-shot SSDA:

    python main.py --num 1 --dataset Office-31 --s webcam --t amazon --gpu_id 0 --train 1

Test on Office-31 1-shot SSDA:

    python main.py --num 1 --dataset Office-31 --s webcam --t amazon --gpu_id 0 --train 0

The other datasets follow the similar usages!

If you find the repo is helpful, feel free to star and cite us:

@article{MA2022270,
title = {Context-guided entropy minimization for semi-supervised domain adaptation},
journal = {Neural Networks},
volume = {154},
pages = {270-282},
year = {2022},
issn = {0893-6080},
doi = {https://doi.org/10.1016/j.neunet.2022.07.011},
url = {https://www.sciencedirect.com/science/article/pii/S0893608022002672},
author = {Ning Ma and Jiajun Bu and Lixian Lu and Jun Wen and Sheng Zhou and Zhen Zhang and Jingjun Gu and Haifeng Li and Xifeng Yan},
}