Resilient Binary Neural Network (ReBNN)

Pytorch implementation of our paper "Resilient Binary Neural Network" accepted by AAAI2023 as oral presentation.

Tips

Any problem, please contact the first author (Email: shengxu@buaa.edu.cn).

Our code is heavily borrowed from ReActNet (https://github.com/liuzechun/ReActNet).

Dependencies

• Python 3.8
• Pytorch 1.7.1
• Torchvision 0.8.2

ReBNN with two-stage tranining

We test our ReBNN using the same ResNet-18 structure and training setttings as ReActNet, and obtain 66.9% top-1 accuracy.

Methods	Top-1 acc	Top-5 acc	Quantized model link	Log
ReActNet	65.9	-	Model	-
ReCU	66.4	86.5	Model	-
RBONN	66.7	87.0	Model	-
ReBNN	66.9	87.1	-	-

To verify the performance of our quantized models with ReActNet-like structure on ImageNet, please do as the following steps:

1. Finish the first stage training using ReActNet.
2. Use the following command:

cd 2_step2_rebnn 
bash run.sh

If you find this work useful in your research, please consider to cite:

@inproceedings{xu2023resilient,
  title={Resilient Binary Neural Network},
  author={Xu, Sheng and Li, Yanjing and Ma, Teli and Lin, Mingbao and Dong, Hao and Zhang, Baochang and Gao, Peng and Lu, Jinhu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  number={9},
  pages={10620--10628},
  year={2023}
}