Semi-supervised-learning-dkl

Decoupled Kullback-Leibler (DKL) Divergence Loss

This repository contains the implementation code for our NeurIPS 2024 paper Decoupled Kullback-Leibler (DKL) Divergence Loss, arXiv.

Results for Semi-supervised Learning

CIFAR-100 with 200 labeled-data

#	Method	Model	Acc	log
1	FixMatch	ViT-Small	69.89	log
2	FixMatch-dkl	ViT-Small	70.57	log
3	MeanTeacher	ViT-Small	67.49	log
4	MeanTeacher-dkl	ViT-Small	68.75	log

Training

Please refer to https://github.com/microsoft/Semi-supervised-learning.git for environment setup.

For the semi-supervised learning task:
cd Semi-supervised-learning-dkl 
bash sh/train_fixmatch_ikl.sh
bash sh/train_meanteacher_ikl.sh

Contact

If you have any questions, feel free to contact us through email (jiequancui@gmail.com) or Github issues. Enjoy!

BibTex

If you find this code or idea useful, please consider citing our related work:

@article{cui2023decoupled,
  title={Decoupled Kullback-Leibler Divergence Loss},
  author={Cui, Jiequan and Tian, Zhuotao and Zhong, Zhisheng and Qi, Xiaojuan and Yu, Bei and Zhang, Hanwang},
  journal={arXiv preprint arXiv:2305.13948},
  year={2023}
}

@inproceedings{cui2021learnable,
  title={Learnable boundary guided adversarial training},
  author={Cui, Jiequan and Liu, Shu and Wang, Liwei and Jia, Jiaya},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={15721--15730},
  year={2021}
}