[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation
Prerequisites
To install requirements:
pip install -r requirements.txt
- Python 3.6
- GPU Memory: 10GB
- Pytorch 1.4.0
Getting Started
Download the dataset: Office-31, OfficeHome, VisDA, DomainNet.
Data Folder structure:
Your dataset DIR:
|-Office/domain_adaptation_images
| |-amazon
| |-webcam
| |-dslr
|-OfficeHome
| |-Art
| |-Product
| |-...
|-VisDA
| |-train
| |-validataion
|-DomainNet
| |-clipart
| |-painting
| |-...
You need you modify the data_path in config files, i.e., config.root
Training
Train on one transfer of Office:
CUDA_VISIBLE_DEVICES=0 python office_run.py note=EXP_NAME setting=uda/osda/pda source=amazon target=dslr
To train on six transfers of Office:
CUDA_VISIBLE_DEVICES=0 python office_run.py note=EXP_NAME setting=uda/osda/pda transfer_all=1
Train on OfficeHome:
CUDA_VISIBLE_DEVICES=0 python officehome_run.py note=EXP_NAME setting=uda/osda/pda source=Art target=Product
or
CUDA_VISIBLE_DEVICES=0 python officehome_run.py note=EXP_NAME setting=uda/osda/pda transfer_all=1
The final results (including the best and the last) will be saved in the ./snapshot/EXP_NAME/result.txt.
Notably, transfer_all wil consumes more shared memory.
Citation
If you find it helpful, please consider citing:
@inproceedings{li2021DCC,
title={Domain Consensus Clustering for Universal Domain Adaptation},
author={Li, Guangrui and Kang, Guoliang and Zhu, Yi and Wei, Yunchao and Yang, Yi},
booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2021}
}