Deep multi-task augmented feature learning via hierarchical graph neural network

Prerequisites:

• Python3
• Tensorflow==1.15.0
• Numpy

Dataset:

For the classification tasks:

• imageclef.txt
• office_caltech_10.txt
• office_home.txt

For the regression tasks:

• sarcos_2000.txt

You can download the pre-processed datasets from the URL below and put them at the directory "./data".

https://drive.google.com/drive/folders/1eqRDifM7tCZ9xnT-f68RwImXKwV9n2YU?usp=sharing

Training:

You can modify the code "datafile=./data/*.txt" in "*.py" before you run the code for training different datasets.

You can run "*_HGNN.py" to train and evaluate on the classification tasks and run "*_HGNN_reg.py" to train and evaluate on the regression tasks.

Citation

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

@inproceedings{guo2021deep,
  title={Deep multi-task augmented feature learning via hierarchical graph neural network},
  author={Guo, Pengxin and Deng, Chang and Xu, Linjie and Huang, Xiaonan and Zhang, Yu},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={538--553},
  year={2021},
  organization={Springer}
}

Contact

If you have any problem about our code, feel free to contact 12032913@mail.sustech.edu.cn.