Fetching the paper…
Reading the bibliography…
Machine unlearning (MU) seeks to remove knowledge of specific data samples from trained models without the necessity for complete retraining, a task made challenging by the dual objectives of effective erasure of data and maintaining the overall performance of the model.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Mantelero, A.: The eu proposal for a general data protection regulation and the roots of the ‘right to be forgotten’. Computer Law & Security Review 29
2013
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning. pp. 1126–1135. PMLR (2017)
2017
Earlier work this paper cites.
Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE symposium on security and privacy (SP). pp. 3–18. IEEE (2017)
2017
Earlier work this paper cites.
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Antoniou, A., Storkey, A.J.: Learning to learn by self-critique. Advances in Neural Information Processing Systems 32
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Rajeswaran, A., Finn, C., Kakade, S.M., Levine, S.: Meta-learning with implicit gradients. Advances in neural information processing systems 32
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
Golatkar, A., Achille, A., Soatto, S.: Eternal sunshine of the spotless net: Selective forgetting in deep networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9304–9312 (2020)
2020
Earlier work this paper cites.
Golatkar, A., Achille, A., Soatto, S.: Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16. pp. 383–398. Springer (2020)
2020
Earlier work this paper cites.
Guo, J., Zhu, X., Zhao, C., Cao, D., Lei, Z., Li, S.Z.: Learning meta face recognition in unseen domains. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6163–6172 (2020)
2020
Earlier work this paper cites.
Nguyen, Q.P., Low, B.K.H., Jaillet, P.: Variational bayesian unlearning. Advances in Neural Information Processing Systems 33
2020
Earlier work this paper cites.
Wu, Y., Dobriban, E., Davidson, S.: Deltagrad: Rapid retraining of machine learning models. In: International Conference on Machine Learning. pp. 10355–10366. PMLR (2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C.A., Jia, H., Travers, A., Zhang, B., Lie, D., Papernot, N.: Machine unlearning. In: 2021 IEEE Symposium on Security and Privacy (SP). pp. 141–159. IEEE (2021)
2021
Earlier work this paper cites.
Chen, M., Zhang, Z., Wang, T., Backes, M., Humbert, M., Zhang, Y.: When machine unlearning jeopardizes privacy. In: Proceedings of the 2021 ACM SIGSAC conference on computer and communications security. pp. 896–911 (2021)
2021
Earlier work this paper cites.
Golatkar, A., Achille, A., Ravichandran, A., Polito, M., Soatto, S.: Mixed-privacy forgetting in deep networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 792–801 (2021)
2021
Earlier work this paper cites.
Graves, L., Nagisetty, V., Ganesh, V.: Amnesiac machine learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 11516–11524 (2021)
2021
Cited alongside, same era.
Huang, C., Cao, Z., Wang, Y., Wang, J., Long, M.: Metasets: Meta-learning on point sets for generalizable representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8863–8872 (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Cited alongside, same era.
Koh, S., Shon, H., Lee, J., Hong, H.G., Kim, J.: Disposable transfer learning for selective source task unlearning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 11752–11760 (2023)
2023
Later among the works it cites.
Lin, S., Zhang, X., Chen, C., Chen, X., Susilo, W.: Erm-ktp: Knowledge-level machine unlearning via knowledge transfer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20147–20155 (2023)
2023
Later among the works it cites.
Liu, J., Xue, M., Lou, J., Zhang, X., Xiong, L., Qin, Z.: Muter: Machine unlearning on adversarially trained models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4892–4902 (2023)
2023
Later among the works it cites.
Pan, J., Foo, L.G., Zheng, Q., Fan, Z., Rahmani, H., Ke, Q., Liu, J.: Gradmdm: Adversarial attack on dynamic networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 45
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Foo, L.G., Li, T., Rahmani, H., Ke, Q., Liu, J.: Era: Expert retrieval and assembly for early action prediction. In: European Conference on Computer Vision. pp. 670–688. Springer (2022)
2022
Cited alongside, same era.
Hu, H., Salcic, Z., Sun, L., Dobbie, G., Yu, P.S., Zhang, X.: Membership inference attacks on machine learning: A survey. ACM Computing Surveys (CSUR) 54
2022
Cited alongside, same era.
Kim, J., Woo, S.S.: Efficient two-stage model retraining for machine unlearning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4361–4369 (2022)
2022
Cited alongside, same era.
Liu, Y., Zhao, Z., Backes, M., Zhang, Y.: Membership inference attacks by exploiting loss trajectory. In: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security. pp. 2085–2098 (2022)
2022
Cited alongside, same era.
Mehta, R., Pal, S., Singh, V., Ravi, S.N.: Deep unlearning via randomized conditionally independent hessians. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10422–10431 (2022)
2022
Cited alongside, same era.
Thudi, A., Deza, G., Chandrasekaran, V., Papernot, N.: Unrolling sgd: Understanding factors influencing machine unlearning. In: 2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P). pp. 303–319. IEEE (2022)
2022
Cited alongside, same era.
Ye, J., Fu, Y., Song, J., Yang, X., Liu, S., Jin, X., Song, M., Wang, X.: Learning with recoverable forgetting. In: European Conference on Computer Vision. pp. 87–103. Springer (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Tarun, A.K., Chundawat, V.S., Mandal, M., Kankanhalli, M.: Fast yet effective machine unlearning. IEEE Transactions on Neural Networks and Learning Systems (2023)
2023
Later among the works it cites.
Xu, H., Zhu, T., Zhang, L., Zhou, W., Yu, P.S.: Machine unlearning: A survey. ACM Comput. Surv. 56
2023
Later among the works it cites.
Xu, L., Huang, M.H., Shang, X., Yuan, Z., Sun, Y., Liu, J.: Meta compositional referring expression segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19478–19487 (2023)
2023
Later among the works it cites.
Xu, L., Liu, J.: Experts collaboration learning for continual multi-modal reasoning. IEEE Transactions on Image Processing (2023)
2023
Later among the works it cites.
Zhang, H., Nakamura, T., Isohara, T., Sakurai, K.: A review on machine unlearning. SN Computer Science 4
2023
Later among the works it cites.
Zhou, J., Li, H., Liao, X., Zhang, B., He, W., Li, Z., Zhou, L., Gao, X.: A unified method to revoke the private data of patients in intelligent healthcare with audit to forget. Nature Communications 14
2023
Later among the works it cites.
2024
Closest in time.
Cha, S., Cho, S., Hwang, D., Lee, H., Moon, T., Lee, M.: Learning to unlearn: Instance-wise unlearning for pre-trained classifiers. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 11186–11194 (2024)
2024
Closest in time.
Chen, R., Yang, J., Xiong, H., Bai, J., Hu, T., Hao, J., Feng, Y., Zhou, J.T., Wu, J., Liu, Z.: Fast model debias with machine unlearning. Advances in Neural Information Processing Systems 36
2024
Closest in time.
2024
Closest in time.
Heng, A., Soh, H.: Selective amnesia: A continual learning approach to forgetting in deep generative models. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Kurmanji, M., Triantafillou, P., Hayes, J., Triantafillou, E.: Towards unbounded machine unlearning. Advances in neural information processing systems 36
2024
Closest in time.
2024
Closest in time.
Peng, D., Xu, L., Ke, Q., Hu, P., Liu, J.: Joint attribute and model generalization learning for privacy-preserving action recognition. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Peng, D., Zhang, Z., Hu, P., Ke, Q., Yau, D., Liu, J.: Harnessing text-to-image diffusion models for category-agnostic pose estimation. In: European Conference on Computer Vision. Springer (2024)
2024
Closest in time.
2024
Closest in time.
Xu, J., Wu, Z., Wang, C., Jia, X.: Machine unlearning: Solutions and challenges. IEEE Transactions on Emerging Topics in Computational Intelligence (2024)
2024
Closest in time.