🧹 Machine Unlearning Comparator

A web-based visual analytics system for the comparative evaluation of Machine Unlearning (MU) methods.

This system helps researchers systematically compare MU methods based on three core principles: accuracy, efficiency, and privacy. The workflow is structured into four stages: Build → Screen → Contrast → Attack.

✨ Key Features

• Multi-Level Visual Comparison

  • Analyze model behavior from class, instance, and layer-level perspectives.
  • Includes: Class-wise Accuracy chart, Prediction Matrix, Embedding Space, and Layer-wise Similarity chart.

• Interactive Privacy Audits

  • Simulate Membership Inference Attacks (MIAs) to verify data removal.

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🔧 Built-in Methods

Method	Description
Fine-Tuning (FT)	Fine-tunes the model only on the retain set.
Gradient Ascent (GA)	Adjusts model parameters to maximize loss on the forget set.
Random Labeling (RL)	Assigns random labels to the forget set and then fine-tunes the model.
SCRUB	Uses a teacher-student distillation framework to maximize loss on the forget set while minimizing it on the retain set.
SalUn	Masks weights influenced by the forget set before applying random labeling and targeted fine-tuning.

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🔌 Add Your Own Method

Implement and register your own MU methods via a Python hook for direct comparison within the system.

💡 Tip: Ask Claude Code for a boilerplate template to get started quickly!

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⚡ Quick Start

Backend

# 1. Install deps & activate environment
hatch shell
# 2. Run the API server
hatch run start

Frontend

# 1 Install deps
pnpm install
# 2 Launch the UI
pnpm start

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Related Resources

• ResNet-18 CIFAR-10 MU checkpoints → https://huggingface.co/jaeunglee/resnet18-cifar10-unlearning
• ResNet-18 FashionMNIST MU checkpoints → https://huggingface.co/Yurim0507/resnet18-fashionmnist-unlearning
• ViT-Base CIFAR-10 MU checkpoints → https://huggingface.co/Yurim0507/vit-base-16-cifar10-unlearning

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📚 Citation

If you use this tool in your research, please cite our paper:

@misc{lee2025unlearning,
  title = {{Unlearning Comparator:} A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods},
  author = {Jaeung Lee and Suhyeon Yu and Yurim Jang and Simon S. Woo and Jaemin Jo},
  year   = {2025},
  note   = {arXiv:2508.12730},
  url    = {https://arxiv.org/abs/2508.12730}
}