Official code for Hessian Aware Low-Rank Perturbation for Order-Robust Continual Learning (HALRP, accepted by IEEE TKDE)

[paper][arxiv]

Experiments

• Prepare datasets:

bash scripts-prepare/download_tinyimgnet.sh
bash scripts-prepare/download_others.sh

• Run HALRP on 5-dataset with AlexNet/ResNet18:

bash scripts/five_AlexNet_HALRP.sh
bash scripts/five_ResNet18_HALRP.sh

• Run HALRP on TinyImageNet 20-split with AlexNet/Resnet18:

bash scripts/tiny20_AlexNet_HALRP.sh
bash scripts/tiny20_ResNet18_HALRP.sh

• Run HALRP on TinyImageNet 40-split with AlexNet/Resnet18:

bash scripts/tiny40_AlexNet_HALRP.sh
bash scripts/tiny40_ResNet18_HALRP.sh

• Run HALRP on CIFAR100-Splits/-SuperClass with LeNet:

### 'TRAINSIZE=1.0' means "100% of training data. 
###   Change this percentage to reproduce the results in Table 2&5 of the paper.
bash scripts/cifar100_splits100_LeNet_HALRP.sh A # (or B/C/D/E for other task orders)
bash scripts/cifar100_super100_LeNet_HALRP.sh A # (or B/C/D/E for other task orders)

• Run HALRP on PMNIST with LeNet:

bash scripts/pmnist_LeNet_HALRP.sh

Citation

If you find it useful for your study, please consider to cite:

@ARTICLE{li2024hessian,
  author={Li, Jiaqi and Lai, Yuanhao and Wang, Rui and Shui, Changjian and Sahoo, Sabyasachi and Ling, Charles X. and Yang, Shichun and Wang, Boyu and Gagné, Christian and Zhou, Fan},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={Hessian Aware Low-Rank Perturbation for Order-Robust Continual Learning}, 
  year={2024},
  volume={},
  number={},
  pages={},
  doi={10.1109/TKDE.2024.3419449}
}

Acknowledgement

This work was finished with Dr. Fan Zhou(@Beihang University) and Prof. Christian Gagné(@Université Laval).