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PyTorch Implementation of HIERMATCH

Official Code release for HIERMATCH: Leveraging Label Hierarchies for Improving Semi-Supervised Learning <br/> Ashima Garg, Shaurya Bagga, Yashvardhan Singh, Saket Anand. <br /> IEEE Winter Conference on Applications of Computer Vision (WACV 2022) <br/>

Citations

If you find this paper useful, please cite our paper:

@InProceedings{Garg_2022_WACV,
    author    = {Garg, Ashima and Bagga, Shaurya and Singh, Yashvardhan and Anand, Saket},
    title     = {HierMatch: Leveraging Label Hierarchies for Improving Semi-Supervised Learning},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2022},
    pages     = {1015-1024}
}

Proposed HIERMATCH

Installation

Clone the repository

$ git clone https://github.com/07Agarg/HIERMATCH
$ cd HIERMATCH

Using the Code

HIERMATCH approach is tested on CIFAR-100 and North American Birds Dataset. <br/>

• To run HIERMATCH on CIFAR-100 (for level-2 and level-3)

  • With samples from finest-grained level only and no additional samples from coarser-levels, use the code folder in HIERMATCH-cifar-100/
  • With samples from finest-grained level and partial-labeled samples from coarser-levels, use the code folder in HIERMATCH-cifar-100-partial/.

• To run HIERMATCH on NABirds (for level-2 and level-3)

  • With samples from finest-grained level only and no additional samples from coarser-levels, use the code folder in HIERMATCH-nabirds/
  • With samples from finest-grained level and partial-labeled samples from coarser-levels, use the code folder in HIERMATCH-nabirds-partial/.

Use the command in the respective folders: python train.py

Acknowledgements

The codebase is borrowed from MixMatch

Contact

If you have any suggestion or question, you can leave a message here or contact us directly at ashimag@iiitd.ac.in