Enhanced Transport Distance for Unsupervised Domain Adaptation (ETD)

This is the pytorch demo code for Enhanced Transport Distance for Unsupervised Domain Adaptation (ETD) (CVPR 2020)

Requirements

• python 3.7
• torch 1.2.0
• torchvision 0.4.0
• pandas 0.24.2
• numpy 1.17.3

Dataset

• The structure of the datasets should be like

OfficeHome (Dataset)
|- Art (Domain)
|  |- Alarm_Clock (Class)
|     |- 00001.jpg (Sample) 
|     |- ...
|  |- Backpack (Class)
|  |- ...
|- Clipart
|- Product 
|- Real_World

• The srtucture of all the code should be like

|- OfficeHome
|- UV_code

Usage

• Download the OfficeHome dataset from Google Drive.

• Set experiment configures in a csv file.

  • It is UV.csv in this code.
  • The csv file includes: epochs, Pretrain_Epoch, train_batch_size, lr lr_feature, lr_fc, beta1, beta2, lambda_1, lambda_2, source_domain, target_domain, class_num, resnet_name, fc_in_features, bottleneck_dim, dropout_p, and network_name.
  • An example is shown as following (configures in this figure may not be the best choices and this figure is just to explain the configure file more clear):

• Set saving path.

  • The saving path is ./UV_code/UV in this code and the corresponding code is shown as following:

   file_path = '.'+os.path.sep+'UV' 
   if not os.path.exists(file_path):
      os.mkdir(file_path)
   experiment_base_path = '.'+os.path.sep+'UV'+os.path.sep+experiment_name        
   if not os.path.exists(experiment_base_path):
      os.mkdir(experiment_base_path)
  

• Training with main.py.

• The loss, acc, best acc and best model can be found in ./UV_code/UV/test1(in this code).

Note

This code is correspongding to the dual formulation of the reweighed OT problem. And we will introduce the semi-dual version later.

Citation

If this reposity is helpful for you, please cite our paper:
@inproceedings{Li2020ETD,
  title={Enhanced Transport Distance for Unsupervised Domain Adaptation},
  author={Mengxue Li, and Yi-Ming Zhai, and You-Wei Luo, and Peng-Fei Ge, and Chuan-Xian Ren},
  booktitle={2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020},
}

Contact

If you have any questions, please feel free to contact me via zhaiym3@mail2.sysu.edu.cn.