Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018)
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
caffe revised Dec 9, 2018
data revised Dec 9, 2018
pytorch Merge branch 'master' of https://github.com/thuml/CDAN Dec 23, 2018
tensorflow revised Dec 5, 2018
.gitignore revised Dec 9, 2018
README.md revised Dec 9, 2018

README.md

CDAN

Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018)

Dataset

Digits

Processed SVHN_dataset is here. We change the original mat into images. Other transformed images are in data/svhn2mnist and data/usps2mnist. Dataset_train.txt are lists for source and target domains and Dataset_test.txt are lists for test.

Office-31

Office-31 dataset can be found here.

Office-Home

Office-Home dataset can be found here.

VisDA-2017

VisDA 2017 dataset can be found here in the classification track.

Image-clef

We release the Image-clef dataset we used here.

Training

Training instructions for Caffe and PyTorch are in the README.md in caffe and pytorch respectively.

Tensorflow version is under developing.

Citation

If you use this code for your research, please consider citing:

@inproceedings{long2018conditional,
  title={Conditional adversarial domain adaptation},
  author={Long, Mingsheng and Cao, Zhangjie and Wang, Jianmin and Jordan, Michael I},
  booktitle={Advances in Neural Information Processing Systems},
  pages={1645--1655},
  year={2018}
}

Contact

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

or describe your problem in Issues.