Generative Model Unlearning

Overview

• Uses task-specific VAEs to replay prior knowledge during continual learning on Split CIFAR-100.
• Covers both incremental training and post-hoc unlearning of selected tasks.
• Historic experiment summaries are stored in results/.

Layout

• src/generative_model_unlearning/ – package with data preparation, generative models, and runners.
• results/ – accuracy logs from earlier experiments.
• requirements.txt – dependencies shared by training scripts.

Quickstart

1. cd repositories/Generative-Continual-Learning-PyTorch
2. python -m venv .venv && source .venv/bin/activate
3. pip install -r requirements.txt
4. export PYTHONPATH=src
5. Train with generative replay: python -m generative_model_unlearning.run_project_c

Unlearning demo

export PYTHONPATH=src
python -m generative_model_unlearning.generative_unlearning

Dynamic Architecture Continual Learning — Generative Unlearning

One-line: research codebase for continual learning experiments using task-specific generative replay (VAEs) on Split CIFAR-100, including post-hoc unlearning experiments.

This repository contains code used to run and reproduce experiments for generative-model aided continual learning and targeted unlearning of learned tasks.

Quick links

• Code: src/generative_model_unlearning/
• Data: data/ (CIFAR-100 stored under data/cifar-100-python/)
• Results and logs: results/
• Python deps: requirements.txt

Requirements

• Python 3.10+ recommended
• PyTorch 2.0+ and torchvision

Install dependencies (recommended inside a virtualenv):

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Set PYTHONPATH so tests and runners can import the package in src:

export PYTHONPATH=$(pwd)/src

Project structure

• src/generative_model_unlearning/ — core package
  • data_setup.py — CIFAR-100 loading and dataset helpers
  • generative_models.py — VAE and related generator code
  • generative_unlearning.py — scripts to run selective unlearning experiments
  • run_project_c.py — example training/run entrypoint
• data/ — external dataset files (not tracked in git)
• results/ — experiment outputs (not tracked in git)
• requirements.txt — pinned runtime dependencies

If you add new data or large model checkpoints, place them under the data/ or results/ directories; these are ignored by git by default.

Usage examples

Train a model (example):

# from repository root
export PYTHONPATH=$(pwd)/src
python -m generative_model_unlearning.run_project_c

Run the unlearning demo:

export PYTHONPATH=$(pwd)/src
python -m generative_model_unlearning.generative_unlearning

Add or change command-line args in the if __name__ == '__main__' blocks of the modules above to customize dataset roots, epochs, or save locations.

Development

• Follow the Python packaging layout: keep code under src/ to simplify imports.
• Create a virtual environment (see above).
• Run unit or smoke tests (not currently provided) by creating a tests/ directory and using pytest.

Suggested small improvements you can add:

• Add a setup.cfg and pyproject.toml for tooling (ruff, black, pytest).
• Add a lightweight test that loads the dataset and runs a single training step to catch API regressions.