Code release for Universal Domain Adaptation(CVPR 2019)
As the focus of my research has moved away from domain adaptation, this code repository may be obsolete someday. We are delighted to see that universal domain adaptation has received tremendous attention in the academic community, and readers are encouraged to discuss related questions with the authors of follow-up papers.
- python 3.6+
- PyTorch 1.0
pip install -r requirements.txt
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download datasets
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write your config file
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python main.py --config /path/to/your/config/yaml/file -
train (configurations in
officehome-train-config.yamlare only for officehome dataset):python main.py --config officehome-train-config.yaml -
test
python main.py --config officehome-test-config.yaml -
monitor (tensorboard required)
tensorboard --logdir .
We provide the checkpoints for officehome datasets at Google Drive.
please cite:
@InProceedings{UDA_2019_CVPR,
author = {You, Kaichao and Long, Mingsheng and Cao, Zhangjie and Wang, Jianmin and Jordan, Michael I.},
title = {Universal Domain Adaptation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}