# Machine Unlearning using a Multi-GAN based Model

arXiv: https://arxiv.org/abs/2407.18467 · HTML: https://mlsanity.com/paper/2407.18467/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 26 Jul 2024.

## Placed in

- LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/)

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Certified Data Removal from Machine Learning Models](https://mlsanity.com/paper/1911.03030/) (2019)
- [Label-Only Membership Inference Attacks](https://mlsanity.com/paper/2007.14321/) (2020)
- [Synthetic Data from Diffusion Models Improves ImageNet Classification](https://mlsanity.com/paper/2304.08466/) (2023)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Approximate Data Deletion from Machine Learning Models](https://mlsanity.com/paper/2002.10077/) (2020)
- [Fast Yet Effective Machine Unlearning](https://mlsanity.com/paper/2111.08947/) (2021)
- [Overcoming Catastrophic Forgetting in Graph Neural Networks](https://mlsanity.com/paper/2012.06002/) (2020)
- [AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models](https://mlsanity.com/paper/1909.09251/) (2019)
- [Machine Unlearning: A Survey](https://mlsanity.com/paper/2306.03558/) (2023)
- [Overcoming Catastrophic Forgetting by Generative Regularization](https://mlsanity.com/paper/1912.01238/) (2019)
- [Ticketed Learning-Unlearning Schemes](https://mlsanity.com/paper/2306.15744/) (2023)
- [Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten](https://mlsanity.com/paper/2302.04288/) (2023)
- [Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities](https://mlsanity.com/paper/2306.12609/) (2023)

## Abstract (arXiv)

This article presents a new machine unlearning approach that utilizes multiple Generative Adversarial Network (GAN) based models. The proposed method comprises two phases: i) data reorganization in which synthetic data using the GAN model is introduced with inverted class labels of the forget datasets, and ii) fine-tuning the pre-trained model. The GAN models consist of two pairs of generators and discriminators. The generator discriminator pairs generate synthetic data for the retain and forget datasets. Then, a pre-trained model is utilized to get the class labels of the synthetic datasets. The class labels of synthetic and original forget datasets are inverted. Finally, all combined datasets are used to fine-tune the pre-trained model to get the unlearned model. We have performed the experiments on the CIFAR-10 dataset and tested the unlearned models using Membership Inference Attacks (MIA). The inverted class labels procedure and synthetically generated data help to acquire valuable information that enables the model to outperform state-of-the-art models and other standard unlearning classifiers.