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Since the recent advent of regulations for data protection (e.g., the General Data Protection Regulation), there has been increasing demand in deleting information learned from sensitive data in pre-trained models without retraining from scratch.
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Van der Maaten, L.; and Hinton, G. 2008 · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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An image is worth 16x16 words: Transformers for image recognition at scale
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Learning fair representations
Zemel, R.; Wu, Y.; Swersky, K.; Pitassi, T.; and Dwork, C. 2013 · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
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Towards making systems forget with machine unlearning
Cao, Y.; and Yang, J. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and¡ 0.5 MB model size
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Adversarial examples in the physical world
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Pruning filters for efficient convnets
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Learning structured sparsity in deep neural networks
Wen, W.; Wu, C.; Wang, Y.; Chen, Y.; and Li, H. 2016 · 2016
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Towards evaluating the robustness of neural networks
Carlini, N.; and Wagner, D. 2017 · 2017
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Densely connected convolutional networks
Huang, G.; Liu, Z.; Van Der Maaten, L.; and Weinberger, K. Q. 2017 · 2017
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J.; Pascanu, R.; Rabinowitz, N.; Veness, J.; Desjardins, G.; Rusu, A. A.; Milan, K.; Quan, J.; Ramalho, T.; Grabska-Barwinska, A.; et al. 2017 · 2017
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Learning efficient convolutional networks through network slimming
Liu, Z.; Li, J.; Shen, Z.; Huang, G.; Yan, S.; and Zhang, C. 2017 · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2017 · 2017
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Age Progression/Regression by Conditional Adversarial Autoencoder
Zhang, Z.; Song, Y.; and Qi, H. 2017 · 2017
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Memory aware synapses: Learning what (not) to forget
Aljundi, R.; Babiloni, F.; Elhoseiny, M.; Rohrbach, M.; and Tuytelaars, T. 2018 · 2018
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Chaudhry, A.; Dokania, P. K.; Ajanthan, T.; and Torr, P. H. 2018 · 2018
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Adversarial weight perturbation helps robust generalization
Wu, D.; Xia, S.-T.; and Wang, Y. 2020 · 2020
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Machine unlearning for random forests
Brophy, J.; and Lowd, D. 2021 · 2021
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CPR: Classifier-Projection Regularization for Continual Learning
Cha, S.; Hsu, H.; Hwang, T.; Calmon, F.; and Moon, T. 2021 · 2021
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Amnesiac machine learning
Graves, L.; Nagisetty, V.; and Ganesh, V. 2021 · 2021
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Natural adversarial examples
Hendrycks, D.; Zhao, K.; Basart, S.; Steinhardt, J.; and Song, D. 2021 · 2021
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Fair feature distillation for visual recognition
Jung, S.; Lee, D.; Park, T.; and Moon, T. 2021 · 2021
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Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; and Chen, L.-C. 2018 · 2018
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An empirical study of example forgetting during deep neural network learning
Toneva, M.; Sordoni, A.; Combes, R. T. d.; Trischler, A.; Bengio, Y.; and Gordon, G. J. 2018 · 2018
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Humans forget, machines remember: Artificial intelligence and the right to be forgotten
Villaronga, E. F.; Kieseberg, P.; and Li, T. 2018 · 2018
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Selfless Sequential Learning
Aljundi, R.; Rohrbach, M.; and Tuytelaars, T. 2019 · 2019
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Making ai forget you: Data deletion in machine learning
Ginart, A.; Guan, M.; Valiant, G.; and Zou, J. Y. 2019 · 2019
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Adversarial examples are not bugs, they are features
Ilyas, A.; Santurkar, S.; Tsipras, D.; Engstrom, L.; Tran, B.; and Madry, A. 2019 · 2019
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Similarity of neural network representations revisited
Kornblith, S.; Norouzi, M.; Lee, H.; and Hinton, G. 2019 · 2019
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Anonymisation models for text data: State of the art, challenges and future directions
Lison, P.; Pilán, I.; Sánchez, D.; Batet, M.; and Øvrelid, L. 2021 · 2021
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Certifiable machine unlearning for linear models
Mahadevan, A.; and Mathioudakis, M. 2021 · 2021
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A survey on bias and fairness in machine learning
Mehrabi, N.; Morstatter, F.; Saxena, N.; Lerman, K.; and Galstyan, A. 2021 · 2021
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Fast yet effective machine unlearning
Tarun, A. K.; Chundawat, V. S.; Mandal, M.; and Kankanhalli, M. 2021 · 2021
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Chundawat, V. S.; Tarun, A. K.; Mandal, M.; and Kankanhalli, M. 2022 · 2022
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What does GPT-3 ”know” about me?
Heikkilä, M. 2022 · 2022
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Efficient Two-Stage Model Retraining for Machine Unlearning
Kim, J.; and Woo, S. S. 2022 · 2022
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Deep Unlearning via Randomized Conditionally Independent Hessians
Mehta, R.; Pal, S.; Singh, V.; and Ravi, S. N. 2022 · 2022
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Architecture matters in continual learning
Mirzadeh, S. I.; Chaudhry, A.; Yin, D.; Nguyen, T.; Pascanu, R.; Gorur, D.; and Farajtabar, M. 2022 · 2022
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Learning with Recoverable Forgetting
Ye, J.; Fu, Y.; Song, J.; Yang, X.; Liu, S.; Jin, X.; Song, M.; and Wang, X. 2022 · 2022
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Few-Shot Unlearning by Model Inversion
Yoon, Y.; Nam, J.; Yun, H.; Kim, D.; and Ok, J. 2022 · 2022
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NeurIPS 2023 Machine Unlearning Challenge
Eleni Triantafillou, e. a., Fabian Pedregosa. 2023 · 2023
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