Introduction

This is official repository for the paper Deep Unlearning: Fast and Efficient Gradient-free Approach to Class Forgetting accepted at TMLR.

Setup Environment using yml

conda env create -f env.yml
conda activate forget

Demo of unlearning algorithm

python3 ./demo.py

Unlearning Single class on CIFAR10, CIFAR100 and ImageNet.

# for CIFAR10
sh ./scripts/our_cifar10.sh

# for CIFAR100
sh ./scripts/our_cifar100.sh

# for ImageNet
sh ./scripts/our_imagenet.sh

Analysis

Scripts for analysis done in the paper can be found in scripts/analysis.

An older version of the repository can be found in the legacy branch of the repository.

Citation

Kindly cite the paper if you use the code. Thanks!

APA

Kodge, Sangamesh, Gobinda Saha, and Kaushik Roy. "Deep Unlearning: Fast and Efficient Gradient-Free Class Forgetting."

Bibtex

@article{
kodge2024deep,
title={Deep Unlearning: Fast and Efficient Gradient-free Class Forgetting},
author={Sangamesh Kodge and Gobinda Saha and Kaushik Roy},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2024},
url={https://openreview.net/forum?id=BmI5p6wBi0},
note={}
}