vision-unlearning 0.1.8

Vision Unlearning: a tool for Machine Unlearning in Computer Vision

Vision Unlearning

Documentation

Installation

pip install vision-unlearning

Compatible with python 3.10 to 3.12.

What is Vision Unlearning?

Vision Unlearning provides a standard interface for unlearning algorithms, datasets, metrics, and evaluation methodologies commonly used in Machine Unlearning for vision-related tasks, such as image classification and image generation.

It bridges the gap between research/theory and engineering/practice, making it easier to apply machine unlearning techniques effectively.

Vision Unlearning is designed to be:

• Easy to use
• Easy to extend
• Architecture-agnostic
• Application-agnostic

Who is it for?

Researchers

For Machine Unlearning researchers, Vision Unlearning helps with:

• Using the same data splits as other works, including the correct segmentation of forget-retain data and generating data with the same prompts.
• Choosing the appropriate metrics for each task.
• Configuring evaluation setups in a standardized manner.

Practitioners

For practitioners, Vision Unlearning provides:

• Easy access to state-of-the-art unlearning algorithms.
• A standardized interface to experiment with different algorithms.

Tutorials

• Replace George W. Bush by Tony Blair using FADE
• Forget cat using UCE (with hyperparam tunning)
• Forget church using Munba The source code for these tutorials is in tutorials/, but their outputs were cleaned to avoid burdening the repo. The links above contain Google Drive stored executions with the full outputs.

For developers: every time there is a relevant modification in the codebase, please run the affected tutorials, save the notebook to Drive, clear the output before commiting.

Main Interfaces

Vision Unlearning standardizes the following components:

• Metric: Evaluates a model (e.g., FID, CLIP Score, MIA, NudeNet, etc.).
• Unlearner: Encapsulates the unlearning algorithm.
• Dataset: Encapsulates the dataset, including data splitting.

Additionally, common tasks and evaluation setups are provided as example notebooks. Several platform integrations, such as Hugging Face and Weights & Biases, are also included.