Energy-Based Models for Continual Learning
This project aims at classification continual learning problems using Energy-Based Models. Mainly based on our paper Energy-Based Models for Continual Learning.
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[Code] This code is the basic version of our paper. We will release the final version soon.
Requirements
The current version of the code has been tested with:
pytorch 1.4.0
torchvision 0.2.1
Training (Boundary-aware setting)
Split MNIST:
EBM:
sh scripts/boundary_aware/train_ebm_splitmnist.sh
Softmax-based classifier:
sh scripts/boundary_aware/train_sbc_splitmnist.sh
Permuted MNIST:
EBM:
sh scripts/boundary_aware/train_ebm_permmnist.sh
Softmax-based classifier:
sh scripts/boundary_aware/train_sbc_permmnist.sh
CIFAR-10:
EBM:
sh scripts/boundary_aware/train_ebm_cifar10.sh
Softmax-based classifier:
sh scripts/boundary_aware/train_sbc_cifar10.sh
CIFAR-100:
EBM:
sh scripts/boundary_aware/train_ebm_cifar100.sh
Softmax-based classifier:
sh scripts/boundary_aware/train_sbc_cifar100.sh
Training (Boundary-agnostic setting)
Split MNIST:
EBM:
sh scripts/boundary_agnostic/train_ebm_splitmnist.sh
Softmax-based classifier:
sh scripts/boundary_agnostic/train_sbc_splitmnist.sh
Permuted MNIST:
EBM:
sh scripts/boundary_agnostic/train_ebm_permmnist.sh
Softmax-based classifier:
sh scripts/boundary_agnostic/train_sbc_permmnist.sh
CIFAR-10:
EBM:
sh scripts/boundary_agnostic/train_ebm_cifar10.sh
Softmax-based classifier:
sh scripts/boundary_agnostic/train_sbc_cifar10.sh
CIFAR-100:
EBM:
sh scripts/boundary_agnostic/train_ebm_cifar100.sh
Softmax-based classifier:
sh scripts/boundary_agnostic/train_sbc_cifar100.sh
Acknowledgements
Parts of the code were based on the implementation of https://github.com/GMvandeVen/continual-learning.
Citation
Please consider citing our papers if you use this code in your research:
@article{li2020energy,
title={Energy-Based Models for Continual Learning},
author={Li, Shuang and Du, Yilun and van de Ven, Gido M and Torralba, Antonio and Mordatch, Igor},
journal={arXiv preprint arXiv:2011.12216},
year={2020}
}