https://github.com/07Agarg/HIERMATCH
Code for the Paper HIERMATCH: Leveraging Label Hierarchies for Improving Semi-Supervised Learning, accepted in WACV 2022
https://github.com/07Agarg/HIERMATCH
deep-learning hierarchy semi-supervised-learning wacv2022
Last synced: 24 days ago
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Code for the Paper HIERMATCH: Leveraging Label Hierarchies for Improving Semi-Supervised Learning, accepted in WACV 2022
- Host: GitHub
- URL: https://github.com/07Agarg/HIERMATCH
- Owner: 07Agarg
- Created: 2021-11-22T05:02:14.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-01-04T10:03:14.000Z (over 3 years ago)
- Last Synced: 2024-11-15T06:32:14.320Z (7 months ago)
- Topics: deep-learning, hierarchy, semi-supervised-learning, wacv2022
- Language: Python
- Homepage:
- Size: 305 KB
- Stars: 6
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# PyTorch Implementation of HIERMATCH
Official Code release for **[HIERMATCH: Leveraging Label Hierarchies for Improving Semi-Supervised Learning](https://arxiv.org/pdf/2111.00164.pdf)**
Ashima Garg, Shaurya Bagga, Yashvardhan Singh, Saket Anand.
_IEEE Winter Conference on Applications of Computer Vision (WACV 2022)_
## 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.
- 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](https://github.com/YU1ut/MixMatch-pytorch)
## Contact
If you have any suggestion or question, you can leave a message here or contact us directly at [email protected]