The code repository for "BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion " [paper]
@inproceedings{wang2023beef,
title={BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion},
author={Wang, Fu-Yun and Zhou, Da-Wei and Liu, Liu and Ye, Han-Jia and Bian, Yatao and Zhan, De-Chuan and Zhao, Peilin},
booktitle={The Eleventh International Conference on Learning Representations},
year={2023}
}The following packages are required to run the scripts:
-
Train CIFAR-100
python main.py --config=./configs/beef/cifar-50-10.json -
Train ImageNet-100
python main.py --config=./configs/beef/imagenet-50-10.json -
Train imbalanced protocols
python main.py --config=./configs/beef/cifar-50-10-random.json # uniform ramdom type python main.py --config=./configs/beef/cifar-50-10-imbalance-1.json # half-half-type python main.py --config=./configs/beef/cifar-50-10-imbalance-0.9.json # exp type
Remember to change YOURDATAROOT into your own data root, or you will encounter errors.
Experimental results show that our method achieves state-of-the-art performance.
| Protocols | Reproduced Avg | Reported Avg |
|---|---|---|
| CIFAR-100 B50 5 steps | 71.75 | 71.70 |
| ImageNet-100 B50 5 steps | 78.48 | 77.27 |
| CIFAR-100 uniform | 70.85 | 71.08 |
| CIFAR-100 half-half | 67.72 | 66.81 |
| CIFAR-100 exp | 68.86 | 67.85 |
If there are any questions, please feel free to contact the author: Fu-Yun Wang (wangfuyun@smail.nju.edu.cn). Enjoy the code.