/WSN

Winning SubNetwork (WSN)

Primary LanguagePythonMIT LicenseMIT

Forget-free Continual Learning with Winning Subnetworks-ICML2022

This is the official implementation of WSN in the paper in Pytorch.

Dependency

Dataset

  • Permuted MNIST (available current version)
  • 5 Datasets (available current version)
  • Omniglot Rotation (available current version)
  • CIFAR-100 Split (available current version)
  • CIFAR-100 Superclass (available current version)
  • TinyImageNet (available current version)

Installation

To execute the codes for running experiments, run the following.

pip install -r requirements.txt

Training

We provide several training examples with this repositories:

  • To train WSN on Permuted MNIST on GPU [GPU_ID] with seed number [SEED] and sparsity [SPARSITY], simply run the following
>> ./scripts/wsn/wsn_pmnist.sh [GPU_ID] [SEED] [SPARSITY]
  • To train WSN on Cifar100-100 on GPU [GPU_ID] with seed number [SEED] and sparsity [SPARSITY], simply run the following
>> ./scripts/wsn/wsn_cifar100_100.sh [GPU_ID] [SEED] [SPARSITY]

References

Haeyong Kang, Rusty John Lloyd Mina, Sultan Rizky Hikmawan Madjid, 
Jaehong Yoon, Mark Hasegawa-Johnson, Sung Ju Hwang, Chang D Yoo., 
Forget-free Continual Learning with Winning Subnetworks-ICML2022