Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation (AAAI 2022)

This is a pytorch Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation.

Environment Requirements

• Python 3.7.0
• Pytorch 1.4.0
• torchvision 0.5.0
• matplotlib
• sklearn
• scipy
• numpy

The data folder should be structured as follows:

├── data/
│   ├── dataset name/     
|   |   ├── domain1/
|   |   ├── domain2/
|   |   ├── domain3/
|   |   ├── .../
│   └── 
├── trained_model/
│   ├── source/     
|   |   ├── dataset name1/
|   |   ├── dataset name2/
|   |   ├── dataset name3/
|   |   ├── .../
│   └── target/
|   |   ├── dataset name1/
|   |   ├── dataset name2/
|   |   ├── dataset name3/
|   |   ├── .../
│   └── final/
|   |   ├── dataset name1/
|   |   ├── dataset name2/
|   |   ├── dataset name3/
|   |   ├── .../

Running on visda dataset

sh run_visda.sh > run_visda.txt 

Running on office-home dataset

sh run_office_home.sh > run_office_home.txt 

Acknowledge

Part of the codes are adapted from BCDM, MCD and SE. We thank them for their excellent projects.

Citation

If you find this code useful please consider citing

@inproceedings{DMCD,
title = {Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation},
author = {Tong Chu and Yahao Liu and Jinhong Deng and Wen Li and Lixin Duan},
booktitle = {Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22)},    
year = {2022}
}