Official implementation of ICCV 2023 paper "Online Prototype Learning for Online Continual Learning".
- python==3.8
- pytorch==1.9.0
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt
CIFAR-10
python main.py --buffer_size 200 --mixup_p 0.6 --mixup_base_rate 0.75 --gpu_id 0
CIFAR-100
python main.py --dataset cifar100 --buffer_size 500 --mixup_p 0.2 --mixup_base_rate 0.9 --gpu_id 0
TinyImageNet
python main.py --dataset tiny_imagenet --buffer_size 1000 --mixup_p 0.2 --mixup_base_rate 0.9 --gpu_id 0
If you found this code or our work useful, please cite us:
@inproceedings{onpro,
title={Online prototype learning for online continual learning},
author={Wei, Yujie and Ye, Jiaxin and Huang, Zhizhong and Zhang, Junping and Shan, Hongming},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={18764--18774},
year={2023}
}