Awesome
LGA
This is the Pytorch Implementation for No One Left Behind: Real-World Federated Class-Incremental Learning
This paper is accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). It is a substantial extension of [CVPR-2022] Federated Class-Incremental Learning
Overview
Requirements:
- python == 3.8
- torch == 1.7.0
- numpy
- PIL
- torchvision == 0.8.1
- cv2
- scipy
- sklearn
Datasets:
-
CIFAR100: You don't need to do anything before running the experiments on CIFAR100 dataset.
-
Imagenet-Subset (Mini-Imagenet): Please manually download the on Mini-Imagenet dataset from the official websites, and place it in './dataset'.
-
Tiny-Imagenet: Please manually download the on Tiny-Imagenet dataset from the official websites, and place it in './dataset'.
Launching an experiment:
For exampler, if you want to run LGA on CIFAR100 in the 10 steps setting:
Modify the path of dataset in './scripts/cifar_task_10.sh' and run the following commands.
sh scripts/cifar_task_10.sh
Citation:
If you find this code is useful to your research, please consider to cite our paper.
@ARTICLE{9616392,
author={Dong, Jiahua and Li, Hongliu and Cong, Yang and Sun, Gan and Zhang, Yulun and Van Gool, Luc},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
title={No One Left Behind: Real-World Federated Class-Incremental Learning},
year={2023}
}
@InProceedings{dong2022federated,
author = {Dong, Jiahua and Wang, Lixu and Fang, Zhen and Sun, Gan and Xu, Shichao and Wang, Xiao and Zhu, Qi},
title = {Federated Class-Incremental Learning},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
}
Contact:
If you have some questions, feel free to contact:
- Jiahua Dong: dongjiahua1995@gmail.com