ToAlign

This is the official implementation for:

ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation,
Guoqiang Wei, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang, Zhibo Chen,
NeurIPS 2021 | arXiv

MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation,
Guoqiang Wei, Cuiling Lan, Wenjun Zeng, Zhibo Chen,
CVPR 2021 | arXiv

Abstract

Unsupervised domain adaptive classifcation intends to improve the classifcation performance on unlabeled target domain. To alleviate the adverse effect of domain shift, many approaches align the source and target domains in the feature space. However, a feature is usually taken as a whole for alignment without explicitly making domain alignment proactively serve the classifcation task, leading to sub-optimal solution. In this paper, we propose an effective Task-oriented Alignment (ToAlign) for unsupervised domain adaptation (UDA). We study what features should be aligned across domains and propose to make the domain alignment proactively serve classifcation by performing feature decomposition and alignment under the guidance of the prior knowledge induced from the classifcation task itself. Particularly, we explicitly decompose a feature in the source domain into a task-related/discriminative feature that should be aligned, and a task-irrelevant feature that should be avoided/ignored, based on the classifcation meta-knowledge.

Usage

Dependency

torch>=1.7.0
torchvision>=0.8.0
termcolor>=1.1.0
yacs>=0.1.8

Train

Single-source UDA on office_home dataset:

# source and target domains can be defined by "--source" and "--target"
python main.py configs/uda_office_home_toalign.yaml --data_root ROOT_TO_OFFICE_HOME --source [a|c|p|r] --target [a|c|p|r] --output_root exp

Multi-source UDA on DomainNet dataset:

 python main.py configs/msda_domainnet_toalign.yaml --data_root ROOT_TO_DOMAINNET --target [c|i|p|q|r|s] --output_root exp

Semi-supervised DA on DomainNet dataset:

Citation

@inproceedings{wei2021toalign,
  title={ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation},
  author={Wei, Guoqiang and Lan, Cuiling and Zeng, Wenjun and Zhang, Zhizheng and Chen, Zhibo},
  booktitle={NeurIPS}
}

@inproceedings{wei2021metaalign,
  title={MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation},
  author={Wei, Guoqiang and Lan, Cuiling and Zeng, Wenjun and Chen, Zhibo},
  booktitle={CVPR},
  pages={16643--16653},
  year={2021}
}

Contributing

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Trademarks

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Acknowledgement

We borrowed some code from GVB and DA_Detection.

weigq/UDA-1