Please refer to the 'run.sh'. We also provide the corresponding log file in the file of './test'
You can start with these examples easily.  

NOTE: Results are based on the ImageNet pre-trained features !!! No additional training involved. 

Note that the A2LP can introduce excellent pseudo labels of unlabeled target data in DA 
(compared to the FC-based classifier and the clustering algorithm). Therefore it could
empower algorithms of DA using pseudo labels of unlabeled target data.

Office-31
|_ amazon
|  |_ back_pack
|     |_ <im-1-name>.jpg
|     |_ ...
|     |_ <im-N-name>.jpg
|  |_ bike
|     |_ <im-1-name>.jpg
|     |_ ...
|     |_ <im-N-name>.jpg
|  |_ ...
|_ dslr
|  |_ back_pack
|     |_ <im-1-name>.jpg
|     |_ ...
|     |_ <im-N-name>.jpg
|  |_ bike
|     |_ <im-1-name>.jpg
|     |_ ...
|     |_ <im-N-name>.jpg
|  |_ ...
|_ ...

@inproceedings{zhang2020label,
  title={Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain Adaptation},
  author={Zhang, Yabin and Deng, Bin and Jia, Kui and Zhang, Lei},
  booktitle={European Conference on Computer Vision},
  pages={781--797},
  year={2020},
  organization={Springer}
}

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

or describe your problem in Issues.