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PFAN

Code for CVPR-2019 paper "Progressive Feature Alignment for Unsupervised Domain Adaptation",We will release a journal version code which further improves the reported results in our paper.We will keep updating this code.

Prerequisites:

Python2/Python3  
Tensorflow 1.10  
Numpy  

Dataset:

You need to download the domain_adaptation_images dataset for test.

Training:

1.run 'train.py' to get the prototype vector  
2.run 'pseudo.py' to get the new train dataset  
3.execute 1&2 alternatively and iteratively  

Citation:

If you use this code for your research, please consider citing:
@InProceedings{PFAN_2019_CVPR,
author = {Chen, Chaoqi and Xie, Weiping and Huang, Wenbing and Rong, Yu and Ding, Xinghao and Huang, Yue and Xu, Tingyang and Huang, Junzhou},
title = {Progressive Feature Alignment for Unsupervised Domain Adaptation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2019}
}

Contact:

If you have any problem about our code, feel free to contact [email protected].

pfan's People

Contributors

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Stargazers

PhilistineComedy avatar  avatar  avatar Nori avatar kingfly avatar Chaoqi Chen avatar  avatar  avatar  avatar Tarun K avatar Tiancheng Zhao (Tony)  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar Jiren Jin avatar  avatar Faiz Ul Wahab avatar Vincent Zhang avatar  avatar  avatar  avatar  avatar schwxd avatar Brown Chen avatar

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