Domain AggRegation Network (DARN)

PyTorch implementation of the ICML 2020 paper Domain Aggregation Networks for Multi-Source Domain Adaptation. Certain codes of this repo are adapted from the MDAN repo. If you have any questions, feel free to email to [email protected].

Prerequisites (Tested Environment)

• Python 3.7.2
• PyTorch 1.4.0
• Numpy 1.18.1
• Matplotlib 3.2.1

How to Run

To produce the plots of the synthetic regression problem in the paper:

python synthetic_regression.py

For the real-world problems (Amazon/Digits/Office-Home), you need to download the corresponding datasets and put the data files in the ./datasets folder.

• Amazon. The dataset is available from the MDAN repo.
• Digits. (The preprocessing code is in digits_prepro.py, with some more dependencies.)
  • MNIST, here we use the PyTorch .pt files.
  • SVHN, using the cropped .mat files.
  • MNIST-M and SynthDigits, using the unpacked version and .mat files.
• Office-Home. To save computation time, the code here is using the pre-trained ResNet-50 features. You can also try the original dataset.

To run the experiment

python main.py --name='amazon'

Then the results will be stored in the ./results folder. You can also run python main.py -h to see more options.

To use DARN for other problems, you need to tune the hyperparameters. Specifically, the following hyperparameters may be important:

• Adversarial loss weight --mu, as suggested by MDAN.
• Inverse temperature parameter --gamma. As discussed in the paper, the temperature $\tau$ balances the weights on different source domains. $\gamma=1/\tau$ is the inverse temperature.

About the L2 Projection

One of the key components is the L2 projection, which is implemented in module.py. The forward pass uses a binary search to find the optimal threshold and saves some essential quantities for the backward pass. The backward pass computes the Jacobian-vector product as described in the appendix of the paper.

Citation

If you find this repo helpful, please give this repo a star and cite our paper:

@inproceedings{wen2020domain,
  title={Domain Aggregation Networks for Multi-Source Domain Adaptation},
  author={Wen, Junfeng and Greiner, Russell and Schuurmans, Dale},
  booktitle={International Conference on Machine Learning},
  pages={10927--10937},
  year={2020}
}