Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks
Open accessAbstract
We explore the problem of selectively forgetting a particular subset of the data used for training a deep neural network. While the effects of the data to be forgotten can be hidden from the output of the network, insights may still be gleaned by probing deep into its weights. We propose a method for "scrubbing" the weights clean of information about a particular set of training data. The method does not require retraining from scratch, nor access to the data originally used for training. Instead, the weights are modified so that any probing function of the weights is indistinguishable from the same function applied to the weights of a network trained without the data to be forgotten. This condition is a generalized and weaker form of Differential Privacy. Exploiting ideas related to the stability of stochastic gradient descent, we introduce an upper-bound on the amount of information remaining in the weights, which can be estimated efficiently even for deep neural networks.
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Most of the above schemes focus on relatively simple machine learning problems, such as linear logistic regression, or complex models with special assumptions.
“Second, most of the above schemes focus on relatively simple machine learning problems, such as linear logistic regression, or complex models with special assumptions [40, 41, 43, 51].”
From Machine Unlearning: A Survey (2024) · cites this paper as [40]
Section 5.1 tests the method on linear logistic regression. The Contributions and Deep Network Scrubbing sections also describe applying it to deep neural networks, while the Contributions section limits the optimality proof to the quadratic case; the Additional information section says the local forgetting bound assumes algorithmic stability. The paper therefore supports the examples of a simple problem and assumptions for a complex model, but cannot establish that *most* of the schemes cited together have this limitation. (LLM, conf=0.82)
Experimental — accuracy not yet measuredModel manipulation methods usually give a verifiability guarantee using theory-based and information bound-based methods.
“Significantly, model manipulation methods usually give a verifiability guarantee using theory-based and information bound-based methods [40, 41, 43].”
From Machine Unlearning: A Survey (2024) · cites this paper as [40]
Section 2 derives a KL-divergence bound that guarantees robustness to any readout function, and Section 5.2 says the authors’ proposed methods provide an upper bound on remaining information. The paper supports a theory- and information-bound-based guarantee for its own forgetting methods, but does not establish that model manipulation methods usually provide such guarantees. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe higher the degree of similarity in model confidence, the better the effect of the unlearning process.
“The higher the degree of similarity, the better the effect of the unlearning process.”
From Machine Unlearning: A Survey (2024) · cites this paper as [40]
Section 5.3 says the scrubbed model’s confidence distribution should resemble that of a model that has never seen the forgotten data. But Section 5.4 warns that favorable output readouts can coexist with very little information actually being removed, so confidence similarity alone does not establish better unlearning. (LLM, conf=0.84)
Experimental — accuracy not yet measuredUnlearning based on model shifting usually removes the impact of the unlearning dataset by calculating the influence on the model.
“Unlearning based on model shifting usually removes the impact of the unlearning dataset by calculating the influence on the model [40, 43].”
From Machine Unlearning: A Survey (2024) · cites this paper as [40]
Section 3.2 describes using gradients and Hessians of the cohort to be forgotten to calculate how its removal affects the model. But Section 4 says the paper’s practical Fisher method does not take the calculated weight-shifting step; it adds noise to the weights instead. The paper therefore supports the general idea of calculating the cohort’s effect, but not the claim that model-shifting methods usually remove it this way. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe information bound metric relies on KL-divergence to measure the information remaining about the unlearning dataset within the model after the unlearning process.
“The information bound metric relies on KL-divergence to measure the information remaining about the unlearning dataset within the model after the unlearning process.”
From Machine Unlearning: A Survey (2024) · cites this paper as [40]
For the data that need to be unlearned, the accuracy should ideally be the same as a model trained without seeing D_u.
“For the data that need to be unlearned, the accuracy should ideally be the same as a model trained without seeing D u [40].”
From Machine Unlearning: A Survey (2024) · cites this paper as [40]
Golatkar et al. devised a new metric for verifying the effectiveness of unlearning schemes by measuring the upper bound of the residual information about samples that need to be unlearned.
“Golatkar et al. [40, 43] devised a new metric for verifying the effectiveness of unlearning schemes, where they measured the upper bound of the residual information about samples that need to be unlearned.”
From Machine Unlearning: A Survey (2024) · cites this paper as [40]
Golatkar et al. [40, 43, 52] verify the effectiveness of their unlearning schemes based on accuracy and relearning time.
“Golatkar et al. [40, 43, 52] verify the effectiveness of their unlearning schemes based on accuracy and relearning time.”
From Machine Unlearning: A Survey (2024) · cites this paper as [40]
In model confidence, they evaluated the similarity of those distributions against the confidence of a trained model that has never seen the unlearning dataset.
“Then they evaluated the similarity of those distributions against the confidence of a trained model that has never seen the unlearning dataset.”
From Machine Unlearning: A Survey (2024) · cites this paper as [40]
Golatkar et al. [40] developed two new verification metrics: model confidence and information bound.
“They also developed two new verification metrics: model confidence and information bound [40].”
From Machine Unlearning: A Survey (2024) · cites this paper as [40]
Model confidence is formulated by measuring the distribution of the entropy of the output predictions on the remaining dataset, the unlearning dataset, and the test dataset.
“Model confidence is formulated by measuring the distribution of the entropy of the output predictions on the remaining dataset, the unlearning dataset, and the test dataset.”
From Machine Unlearning: A Survey (2024) · cites this paper as [40]
Activation distance is a similar metric to model confidence.
“This is a similar metric to model confidence [40].”
From Machine Unlearning: A Survey (2024) · cites this paper as [40]
Section 5.3, “Readout functions used” (page not provided), defines model confidence as the entropy of the output prediction. Searches for “activation distance,” distances between activations, and activation similarity found no passage comparing that metric with model confidence, so our copy does not establish the claimed similarity. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning
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Right to be forgotten in the Era of large language models: implications, challenges, and solutions
Zhang, D., Finckenberg-Broman, P., Hoang, T., Pan, S., Xing, Z., Staples, M., & Xu, X. (2024) · AI and Ethics · doi:10.1007/s43681-024-00573-9 · Cited by 47 · see citing works
Learning to Unlearn: Instance-Wise Unlearning for Pre-trained Classifiers
Cha, S., Cho, S., Hwang, D., Lee, H., Moon, T., & Lee, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.28996 · Cited by 47 · see citing works
Learning with Selective Forgetting
Shibata, T., Irie, G., Ikami, D., & Mitsuzumi, Y. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/137 · Cited by 47 · see citing works
Verifying in the Dark: Verifiable Machine Unlearning by Using Invisible Backdoor Triggers
Guo, Y., Zhao, Y., Hou, S., Wang, C., & Jia, X. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3328269 · Cited by 44 · see citing works
Making recommender systems forget: Learning and unlearning for erasable recommendation
Li, Y., Chen, C., Zheng, X., Liu, J., & Wang, J. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111124 · Cited by 55 · see citing works
ERM-KTP: Knowledge-Level Machine Unlearning via Knowledge Transfer
Lin, S., Zhang, X., Chen, C., Chen, X., & Susilo, W. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01929 · Cited by 41 · see citing works
Machine Unlearning via Representation Forgetting With Parameter Self-Sharing
Wang, W., Zhang, C., Tian, Z., & Yu, S. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3331239 · Cited by 36 · see citing works
Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
Li, N., Zhou, C., Gao, Y., Chen, H., Zhang, Z., Kuang, B., & Fu, A. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3530988 · Cited by 53 · see citing works
GDPR and Large Language Models: Technical and Legal Obstacles
Feretzakis, G., Vagena, E., Kalodanis, K., Peristera, P., Kalles, D., & Anastasiou, A. (2025) · Future Internet · doi:10.3390/fi17040151 · Cited by 38 · see citing works
Selective and collaborative influence function for efficient recommendation unlearning
Li, Y., Chen, C., Zheng, X., Zhang, Y., Gong, B., Wang, J., & Chen, L. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121025 · Cited by 36 · see citing works
Exact-Fun: An Exact and Efficient Federated Unlearning Approach
Xiong, Z., Li, W., Li, Y., & Cai, Z. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00188 · Cited by 33 · see citing works
Adversarial attacks and defenses on text-to-image diffusion models: A survey
Zhang, C., Hu, M., Li, W., & Wang, L. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102701 · Cited by 33 · see citing works
Certified Edge Unlearning for Graph Neural Networks
Wu, K., Shen, J., Ning, Y., Wang, T., & Wang, H. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599271 · Cited by 32 · see citing works
Aldaghri, N., Mahdavifar, H., & Beirami, A. (2021) · IEEE Access · doi:10.1109/access.2021.3090019 · Cited by 32 · see citing works
BFU: Bayesian Federated Unlearning with Parameter Self-Sharing
Wang, W., Tian, Z., Zhang, C., Liu, A., & Yu, S. (2023) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3579856.3590327 · Cited by 31 · see citing works
Learn to Unlearn: Insights Into Machine Unlearning
Qu, Y., Yuan, X., Ding, M., Ni, W., Rakotoarivelo, T., & Smith, D. B. (2024) · Computer · doi:10.1109/mc.2023.3333319 · Cited by 30 · see citing works
Proof of Unlearning: Definitions and Instantiation
Weng, J., Yao, S., Du, Y., Huang, J., Weng, J., & Wang, C. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3358993 · Cited by 30 · see citing works
A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services
Hu, H., Wang, S., Chang, J., Zhong, H., Sun, R., Hao, S., Zhu, H., & Xue, M. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.24252 · Cited by 37 · see citing works
FRAMU: Attention-Based Machine Unlearning Using Federated Reinforcement Learning
Shaik, T., Tao, X., Li, L., Xie, H., Cai, T., Zhu, X., & Li, Q. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3382726 · Cited by 28 · see citing works
Unlearning Graph Classifiers with Limited Data Resources
Pan, C., Chien, E., & Milenković, O. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583547 · Cited by 26 · see citing works
Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
Li, Y., Chen, C., Zheng, X., Zhang, Y., Han, Z., Meng, D., & Wang, J. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612418 · Cited by 26 · see citing works
KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment
Wang, L., Chen, T., Yuan, W., Zeng, X., Wong, K.-F., & Yin, H. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.740 · Cited by 25 · see citing works
Zhong, Z., Bao, W., Wang, J., Zhang, S., Zhou, J., Lyu, L., & Lim, W. Y. B. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02855 · Cited by 26 · see citing works
Hoang, T., Rana, S., Gupta, S., & Venkatesh, S. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00475 · Cited by 23 · see citing works
Deletion inference, reconstruction, and compliance in machine (un)learning
Gao, J., Garg, S., Mahmoody, M., & Vasudevan, P. N. (2022) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2022-0079 · Cited by 22 · see citing works
Zhou, J., Li, H., Liao, X., Zhang, B., He, W., Li, Z., Zhou, L., & Gao, X. (2023) · Nature Communications · doi:10.1038/s41467-023-41703-x · Cited by 22 · see citing works
Kim, H., Lee, S. T., & Woo, S. S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i19.30118 · Cited by 21 · see citing works
An overview of machine unlearning
Li, C., Jiang, H., Chen, J., Zhao, Y. L., Fu, S., Jing, F., & Guo, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100254 · Cited by 21 · see citing works
Forget-SVGD: Particle-Based Bayesian Federated Unlearning
Gong, J., Kang, J., Simeone, O., & Kassab, R. (2022) · IEEE Data Science Workshop (DSW) · doi:10.1109/dslw53931.2022.9820602 · Cited by 21 · see citing works
Fast Model Update for IoT Traffic Anomaly Detection With Machine Unlearning
Fan, J., Wu, K., Zhou, Y., Zhao, Z., & Huang, S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3214840 · Cited by 20 · see citing works
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data
Kurmanji, M., & Triantafillou, P. (2023) · Proceedings of the ACM on Management of Data · doi:10.1145/3588713 · Cited by 21 · see citing works
Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models
Poppi, S., Poppi, T., Cocchi, F., Cornia, M., Baraldi, L., & Cucchiara, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73668-1_20 · Cited by 22 · see citing works
ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach
Hu, Y., Lou, J., Liu, J., Ni, W., Lin, F., Qin, Z., & Ren, K. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3670398 · Cited by 20 · see citing works
To be forgotten or to be fair: unveiling fairness implications of machine unlearning methods
Zhang, D., Pan, S., Hoang, T., Xing, Z., Staples, M., Xu, X., Yao, L., Lu, Q., & Zhu, L. (2024) · AI and Ethics · doi:10.1007/s43681-023-00398-y · Cited by 21 · see citing works
Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation
Xu, H. Y., Zhu, T., Zhang, L., Zhou, W., & Yu, P. S. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3409947 · Cited by 19 · see citing works
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience
Huynh, T. T., Nguyen, T. B., Nguyen, P. L., Nguyên, T. T., Weidlich, M., Nguyen, Q. V. H., & Aberer, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70362-1_4 · Cited by 19 · see citing works
Continual Forgetting for Pre-Trained Vision Models
Zhao, H., Ni, B., Fan, J., Wang, Y., Chen, Y., Meng, G., & Zhang, Z. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02705 · Cited by 23 · see citing works
Get Rid of Your Trail : Remotely Erasing Backdoors in Federated Learning
Alam, M., Lamri, H., & Maniatakos, M. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3465441 · Cited by 19 · see citing works
Have You Forgotten? A Method to Assess if Machine Learning Models Have Forgotten Data
Liu, X., & Tsaftaris, S. A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59710-8_10 · Cited by 18 · see citing works
DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning
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CPR: Retrieval Augmented Generation for Copyright Protection
Golatkar, A., Achille, A., Zancato, L., Wang, Y., Swaminathan, A. D., & Soatto, S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01176 · Cited by 20 · see citing works
Self-citationMachine Unlearning for Image Retrieval: A Generative Scrubbing Approach
Zhang, P.-F., Bai, G., Huang, Z., & Xu, X.-S. (2022) · Proceedings of the 30th ACM International Conference on Multimedia · doi:10.1145/3503161.3548378 · Cited by 18 · see citing works
Generative Unlearning for Any Identity
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NoT: Federated Unlearning via Weight Negation
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MUter: Machine Unlearning on Adversarially Trained Models
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Learning to Unlearn for Robust Machine Unlearning
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A survey on machine unlearning: Techniques and new emerged privacy risks
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Verifiable and Provably Secure Machine Unlearning
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Federated UnlearningPreprint
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Machine Unlearning in Gradient Boosting Decision Trees
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Machine Un-learning: An Overview of Techniques, Applications, and Future Directions
Sai, S., Mittal, U., Chamola, V., Huang, K., Spinelli, I., Scardapane, S., Tan, Z., & Hussain, A. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10219-3 · Cited by 15 · see citing works
- Authors
- Golatkar, A., Achille, A., & Soatto, S.
- Affiliations
- University of California, Los Angeles (US)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 9301-9309
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2985940692
- Semantic Scholar
- c956d133b78e0d9b20885593809f0b636ce34093
- Cited by
- 394
- References
- 32
- Citations by year
- 2026: 96 · 2025: 115 · 2024: 75 · 2023: 56 · 2022: 24 · 2021: 21 · 2020: 6 · 2019: 1
- FWCI
- 10.22
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Office of Naval Research (ONR N00014-17-1-2072, N00014-19-1-2229, ONR-N00014, N00014-17-1, N00014-19, N00014-19-1-2066, N00014-17-1-2072, N00014, N00014-17)
- Army Research Office (W911NF-17, W911NF-17-1-0304, W911NF, ARO W911NF-17-1-0304)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Keywords
- selective forgetting, deep neural network, differential privacy, information erasure, information leakage, privacy-preserving machine learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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