/CAF

[TKDE 2022] A Collaborative Alignment Framework of Transferable Knowledge Extraction for Unsupervised Domain Adaptation

Primary LanguagePythonMIT LicenseMIT

A Collaborative Alignment Framework for Unsupervised Domain Adaptation (TKDE 2022)

by Binhui Xie, Shuang Li, Fangrui Lv, Chi Harold Liu, Guoren Wang, and Dapeng Wu

This repo contains the official PyTorch code and models for the CAF.

Update on 2022/06/24: Paper is available under the "Early Access" area on IEEE Xplore.

One sentence

In this work, we propose a unified framework, called Collaborative Alignment Framework (CAF), which simultaneously reduces the global domain discrepancy and preserves the local semantic consistency for cross-domain knowledge transfer in a collaborative manner.

framework

If you find this project useful in your research, please consider citing:

@ARTICLE{xie2022caf,
  author={Binhui Xie, Shuang Li, Fangrui Lv, Chi Harold Liu, Guoren Wang, and Dapeng Wu},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={A Collaborative Alignment Framework of Transferable Knowledge Extraction for Unsupervised Domain Adaptation},
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TKDE.2021.3118111}
}

Setup Environment

For this project, we used python 3.7.5. We recommend setting up a new virtual environment:

Step-by-step installation

conda create --name CAF -y python=3.7
conda activate CAF

# this installs the right pip and dependencies for the fresh python
conda install -y ipython pip

pip install -r requirements.txt

Setup Datasets

The data folder should be structured as follows:

├── data/
│   ├── domainnet/
|   |   ├── clipart/
|   |   ├── infograph/
|   |   ├── painting/
|   |   ├── quickdraw/
|   |   ├── real/
|   |   ├── sketch/
│   ├── visda2017/clf/
|   |   ├── train/
|   |   ├── validation/
│   ├── office31/	
|   |   ├── amazon/
|   |   ├── dslr/
|   |   ├── webcam/	
│   ├── imageCLEF/     
|   |   ├── c/
|   |   ├── i/
|   |   ├── p/
│   └──

Symlink the required dataset

ln -s /path_to_domainnet_dataset/ data/domainnet
ln -s /path_to_visda2017_dataset/clf/ data/visda2017
ln -s /path_to_office31_dataset data/office31
ln -s /path_to_imageCLEF_dataset data/imageCLEF

Running the code

For DomainNet (Table 2)

bash scripts/domainnet_res50.sh
bash scripts/domainnet_res101.sh

For VisDA2017 (Table 3)

bash scripts/visda.sh

For Office31 (Table 4)

bash scripts/office.sh

For ImageCLEF (Table 5)

bash scripts/celf.sh

Acknowledgements

This project is based on the following open-source projects. We thank their authors for making the source code publicly available.

Contact

If you have any problem about our code, feel free to contact

or describe your problem in Issues.