Local Continual Learning

Patryk Krukowski, Jan Miksa @ GMUM JU

🚀 Let's forget about catastrophic forgetting! 🚀

Work in progress... There may be bugs and features might be missing.

Features

• Hydra Configuration
• WANDB Logging
• Lightning Fabric
• Custom Plugin System for Methods
• Incremental Classifier

Method	Status	Custom Layers	Status	Model	Status	Scenario	Status	Dataset	Status
Naive	✅	Local	✅	MLP	✅	CI	✅	MNIST	✅
LwF	✅	RBF	✅	LeNet	⭕️	DI	✅	ImageNet	✅
EWC	✅	SingleRBFHead	✅			TI	✅	CIFAR100	✅
Sharpening	✅	MultiRBFHead	✅			II	✅	TinyImageNet	✅
SI	✅	KAN	❌			Permuted	⭕️
MAS	✅	LocalHead	⭕️
RBFReg	✅	LocalConv2D	⭕️

Results

SplitMNIST

Method	Full MLP	RBF+MultiRBFHead	RBF+SingleRBFHead	MLP+MultiRBFHead
Naive	19.94			19.95
LwF	39.91		NA	NA
EWC	54.65	19.74	21.86	51.81
SI	32.32	12.62	19.32	31.56
MAS	36.51	10.18	26.58	49.24
Sharpening	19.93
NReg	NA

Commands

Setup

conda create -n "lcl" python=3.9
pip install -r requirements.txt
cp example.env .env
edit .env

Launching Experiments

conda activate lcl
WANDB_MODE={offline/online} HYDRA_FULL_ERROR={0/1} python src/main.py --config-name config 

Acknowledgements

• Project Structure based on template by Bartłomiej Sobieski
• PyTorchRBFLayer repo by Alessio Russo