@inproceedings{hou2021visualizing,
  title={Visualizing Adapted Knowledge in Domain Transfer},
  author={Hou, Yunzhong and Zheng, Liang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2021}
}

This repo dedicates to visualize the learned knowledge in domain adaptation. To understand the adaptation process, we portray the knowledge difference between the source and target model with image translation, using the source-free image translation (SFIT) method proposed in our CVPR2021 paper Visualizing Adapted Knowledge in Domain Transfer.

Specifically, we feed the generated source-style image to the source model, and the original target image to the target model, formulating two branches respectively. Through update the generated image, we force similar outputs between the two branches. When such requirements are met, the image difference should compensate for and can represent the knowledge difference between models.

• Dependencies
• Data Preparation
• Run the Code
  • Train source and target models
  • Visualization

This code uses the following libraries

• python 3.7+
• pytorch 1.6+ & torchvision
• numpy
• matplotlib
• pillow
• scikit-learn

By default, all datasets are in ~/Data/. We use digits (automatically downloaded), Office-31, and VisDA datasets.

Your ~/Data/ folder should look like this

Data
├── digits/
│   └── ...
├── office31/ 
│   └── ...
└── visda/
    └── ...

Once the data preparation is finished, you can train source and target models using unsupervised domain adaptation (UDA) methods

python train_DA.py -d digits --source svhn --target mnist

Currently, we support MMD --da_setting mmd, ADDA --da_setting adda, and SHOT --da_setting shot.

Based on the trained source and target models, we visualize their knowledge difference via SFIT

python train_SFIT.py -d digits --source svhn --target mnist