One-Shot Machine Unlearning with Mnemonic Code

Labeled faces in the wild: A database for studying face recognition in unconstrained environments

Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller · 2007

Earlier work this paper cites.

Imagenet: A large-scale hierarchical image database

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009

Earlier work this paper cites.

Learning multiple layers of features from tiny images

Alex Krizhevsky and Geoffrey Hinton · 2009

Earlier work this paper cites.

The Caltech-UCSD Birds-200-2011 Dataset

Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011

Earlier work this paper cites.

3d object representations for fine-grained categorization

Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013

Earlier work this paper cites.

Towards making systems forget with machine unlearning

Yinzhi Cao and Junfeng Yang · 2015

Earlier work this paper cites.

Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016

Earlier work this paper cites.

Overcoming catastrophic forgetting in neural networks

James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017

Earlier work this paper cites.

Membership inference attacks against machine learning models

Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017

Earlier work this paper cites.

Aggregated residual transformations for deep neural networks

Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017

Cited alongside, same era.

Note on the quadratic penalties in elastic weight consolidation

Ferenc Huszár · 2018

Cited alongside, same era.

Online structured laplace approximations for overcoming catastrophic forgetting

Hippolyt Ritter, Aleksandar Botev, and David Barber · 2018

Cited alongside, same era.

Certified data removal from machine learning models

Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten · 2020

Cited alongside, same era.

Machine unlearning

Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021

Cited alongside, same era.

Extracting training data from large language models

Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021

Backdoor learning: A survey

Yiming Li, Yong Jiang, Zhifeng Li, and Shu-Tao Xia · 2022

Later among the works it cites.

A survey of machine unlearning

Original

Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, Alan Wee-Chung Liew, Hongzhi Yin, and Quoc Viet Hung Nguyen · 2022

Later among the works it cites.

Arcane: An efficient architecture for exact machine unlearning

Haonan Yan, Xiaoguang Li, Ziyao Guo, Hui Li, Fenghua Li, and Xiaodong Lin · 2022

Later among the works it cites.

Extracting training data from diffusion models

Nicolas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023

Closest in time.

Zero-shot machine unlearning

Vikram S Chundawat, Ayush K Tarun, Murari Mandal, and Mohan Kankanhalli · 2023

Closest in time.

Erm-ktp: Knowledge-level machine unlearning via knowledge transfer