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Code for CVPR-2019 paper "Progressive Feature Alignment for Unsupervised Domain Adaptation"

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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 Xiewp67@stu.xmu.edu.cn.

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