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We consider a practical scenario of machine unlearning to erase a target dataset, which causes unexpected behavior from the trained model.
Nonlinear total variation based noise removal algorithms
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Learning multiple layers of features from tiny images
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Learning from partial labels
Cour, T., Sapp, B., and Taskar, B · 2011
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Finding a “kneedle” in a haystack: Detecting knee points in system behavior
Satopaa, V., Albrecht, J., Irwin, D., and Raghavan, B · 2011
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Towards making systems forget with machine unlearning
Cao, Y. and Yang, J · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M., Jha, S., and Ristenpart, T · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
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Identity mappings in deep residual networks
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Right to be forgotten and public registers – A request to the European court of justice for a preliminary ruling
Mantelero, A · 2016
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Dual discriminator adversarial distillation for data-free model compression
Zhao, H., Sun, X., Dong, J., Manic, M., Zhou, H., and Yu, H · 2016
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Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
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Deep image prior
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Data-free learning of student networks
Chen, H., Wang, Y., Xu, C., Yang, Z., Liu, C., Shi, B., Xu, C., Xu, C., and Tian, Q · 2019
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Lifelong anomaly detection through unlearning
Du, M., Chen, Z., Liu, C., Oak, R., and Song, D · 2019
Variational Bayesian unlearning
Nguyen, Q. P., Low, B. K. H., and Jaillet, P · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Yin, H., Molchanov, P., Alvarez, J. M., Li, Z., Mallya, A., Hoiem, D., Jha, N. K., and Kautz, J · 2020
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Fu, S., He, F., Xu, Y., and Tao, D · 2021
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Gupta, V., Jung, C., Neel, S., Roth, A., Sharifi-Malvajerdi, S., and Waites, C · 2021
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Gradient inversion with generative image prior
Jeon, J., Kim, J., Lee, K., Oh, S., and Ok, J · 2021
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Adversarial neural network inversion via auxiliary knowledge alignment
Yang, Z., Chang, E.-C., and Liang, Z · 2019
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Knowledge extraction with no observable data
Yoo, J., Cho, M., Kim, T., and Kang, U · 2019
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Data-free network quantization with adversarial knowledge distillation
Choi, Y., Choi, J., El-Khamy, M., and Lee, J · 2020
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Amnesiac machine learning
Graves, L., Nagisetty, V., and Ganesh, V · 2020
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Large-scale generative data-free distillation
Luo, L., Sandler, M., Lin, Z., Zhmoginov, A., and Howard, A · 2020
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Can bad teaching induce forgetting? Unlearning in deep networks using an incompetent teacher
Chundawat, V. S., Tarun, A. K., Mandal, M., and Kankanhalli, M
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Tarun, A. K., Chundawat, V. S., Mandal, M., and Kankanhalli, M · 2021
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Conditional generative data-free knowledge distillation based on attention transfer
YU, X., Yan, L., and Ou, L · 2021
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Machine unlearning: Linear filtration for logit-based classifiers
Baumhauer, T., Schöttle, P., and Zeppelzauer, M · 2022
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Efficient two-stage model retraining for machine unlearning
Kim, J. and Woo, S. S · 2022
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Learning with recoverable forgetting
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