WADN

Wasserstein Aggregation Domain Network A pytorch implementation of Aggregating From Multiple Target-Shifted Sources

Prerequisites

• Pytorch >=1.0, Torchvision >=0.2
• Scikit-learn >= 0.19.1
• CVXPY>=1.9

Models

• 'was_main_labeled.py ': Evaluation with limited target label prediction
• 'was_main_uda.py': Code for Unsupervised DA
• 'solver.py' Solver for estimating the optimal weights and label distribution ratio

How to cite

@InProceedings{pmlr-v139-shui21a,
  title = 	 {Aggregating From Multiple Target-Shifted Sources},
  author =       {Shui, Changjian and Li, Zijian and Li, Jiaqi and Gagn{\'e}, Christian and Ling, Charles X and Wang, Boyu},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {9638--9648},
  year = 	 {2021},
  editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v139/shui21a/shui21a.pdf},
  url = 	 {http://proceedings.mlr.press/v139/shui21a.html}
}