/GTTN-NGTTN

The implementation of "Multi-task learning via generalized tensor trace norm" [KDD 2021] and "Learning Linear and Nonlinear Low-Rank Structure in Multi-Task Learning" [TKDE 2022]

Primary LanguagePython

Learning Linear and Nonlinear Low-Rank Structure in Multi-Task Learning

Prerequisites

  • Python3
  • PyTorch ==1.6.0
  • Numpy

Datasets

  • fc7 layer feature:
    • office_31_fc7.txt
    • image_clef_fc7.txt
    • office_Home_fc7.txt
    • office-catech_fc7.txt
    • DomainNet_fc7.txt
  • pool5 layer feature:
    • office_31_pool5.txt
    • image_clef_pool5.txt
    • office_Home_pool5.txt
    • office-catech_pool5.txt
    • DomainNet_pool5.txt

Download the pre-processed fc7 layer feature and pool5 layer feature from below link.

https://drive.google.com/drive/folders/1mAc2MPMIzChruQ6SBUC1eHB3XLSaDXvK?usp=sharing

Training

  1. Downloading the dataset(s) from above link.

  2. Run the experiment(s) (task fc7 layer as example):

    cd fc7
python main.py

    To run this experiment, you need to modify the path datafile = 'xxx.txt' before you run python main.py.

Citation

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

@inproceedings{zhang2021multi,
  title={Multi-task learning via generalized tensor trace norm},
  author={Zhang, Yi and Zhang, Yu and Wang, Wei},
  booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
  pages={2254--2262},
  year={2021}
}

@article{zhang2022learning,
  title={Learning Linear and Nonlinear Low-Rank Structure in Multi-Task Learning},
  author={Zhang, Yi and Zhang, Yu and Wang, Wei},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2022},
  publisher={IEEE}
}

Contact

If you have any problem about our code, feel free to contact 11930380@mail.sustech.edu.cn.