Elastic Weight Consolidation (EWC)

Overview

• Focuses on protecting prior knowledge while sequentially learning Split CIFAR-100 tasks.
• Provides both continual learning training and an explicit unlearning workflow.
• Results from earlier experiments are archived under results/.

Layout

• src/ewc/ – package with training entrypoints (main.py, ewc_unlearning.py) plus helpers.
• results/ – text summaries collected from previous experiment runs.
• requirements.txt – minimal dependency set for the method.

Quickstart

1. cd repositories/Regularization-Continual-Learning-PyTorch
2. python -m venv .venv && source .venv/bin/activate
3. pip install -r requirements.txt
4. export PYTHONPATH=src
5. Run continual training: python -m ewc.main

Unlearning demo

export PYTHONPATH=src
python -m ewc.ewc_unlearning

Elastic Weight Consolidation (EWC)

Overview

This repository provides a reference implementation of Elastic Weight Consolidation (EWC) for continual learning experiments on Split CIFAR-100. It includes training scripts, an explicit unlearning workflow, helper utilities for dataset splits, and example experiment outputs in results/.

Repository layout

• src/ewc/ – Python package with training and unlearning entry points (main.py, ewc_unlearning.py) and supporting modules (utils.py, strategies.py).
• results/ – experiment output summaries and logs.
• requirements.txt – Python dependencies used for development and experiments.

Quick start

1. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate

2. Install dependencies:

pip install -r requirements.txt

3. Make the package importable and run the training example:

export PYTHONPATH=src
python -m ewc.main

Unlearning demo

export PYTHONPATH=src
python -m ewc.ewc_unlearning

Data

By default the code downloads CIFAR-100 into ./data. If you prefer a different location, update the root argument in src/ewc/utils.py or set up a data directory and point the scripts to it.

Contributing and development

• Tests: A minimal smoke test is included under tests/ that verifies the package import. Run tests with pytest (install pytest in your environment).
• Packaging: Basic pyproject.toml and setup.cfg are provided for local installation using pip install -e ..
• Style and linting: The project does not enforce a style guide currently; adding pre-commit and flake8/ruff is recommended for collaborators.

Notes

• The implementation is intended as a research reference and is not production hardened. Review training loops, device placement, and checkpointing before using it for large-scale experiments.
• If you want, I can add CI (GitHub Actions) for tests and linting, and a more complete example notebook demonstrating an experiment run.