Forget-free Continual Learning with Winning Subnetworks-ICML2022

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

Dependency

• PyTorch > 1.5

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]

• To train WSN + FSO on Cifar100-100 on GPU [GPU_ID] with seed number [SEED] and sparsity [SPARSITY], simply run the following

>> update soon

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