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Code released for ICML 2019 paper "Bridging Theory and Algorithm for Domain Adaptation".
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README.md Update README.md Jun 14, 2019

README.md

Margin Disparity Discrepancy

Prerequisites:

  • Python3
  • PyTorch ==0.3.1 (with suitable CUDA and CuDNN version)
  • torchvision == 0.2.0
  • Numpy
  • argparse
  • PIL
  • tqdm

Dataset:

You need to modify the path of the image in every ".txt" in "./data".

Training:

You can run "./scripts/train.sh" to train and evaluate on the task. Before that, you need to change the project root, dataset (Office-Home or Office-31), data address and CUDA_VISIBLE_DEVICES in the script.

Citation:

If you use this code for your research, please consider citing:

@inproceedings{MDD_ICML_19,
  title={Bridging Theory and Algorithm for Domain Adaptation},
  author={Zhang, Yuchen and Liu, Tianle and Long, Mingsheng and Jordan, Michael},
  booktitle={International Conference on Machine Learning},
  pages={7404--7413},
  year={2019}
}

Contact

If you have any problem about our code, feel free to contact zhangyuc17@mails.tsinghua.edu.cn.

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