Paper Citation Record · LEDGER

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2506.19486.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.19486 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:12:35.884022Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

verified exact2
verified fuzzy31
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 01a677c3-0c3e-4c20-8803-49c971d92ac3 · outbound

This paper cites Image classification with deep learning in the presence of noisy labels: A survey.Knowledge-Based Systems, 215:106771,.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Image classification with deep learning in the presence of noisy labels: A survey.Knowledge-Based Systems, 215:106771,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.274781Z

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Observation dc758c91-cde7-4443-8bc9-eff8f14d5eda · outbound

This paper cites To- wards making systems forget with machine unlearning.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy To- wards making systems forget with machine unlearning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.259567Z

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Observation 0c54a338-045f-4600-ba08-a436ec52fe34 · outbound

This paper cites When machine unlearning jeopardizes privacy.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy When machine unlearning jeopardizes privacy

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.243016Z

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Observation a06ba731-14d9-4494-8e1d-44c6cca8399c · outbound

This paper cites [Chenet al., 2023 ] Min Chen, Weizhuo Gao, Gaoyang Liu, Kai Peng, and Chen Wang.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy [Chenet al., 2023 ] Min Chen, Weizhuo Gao, Gaoyang Liu, Kai Peng, and Chen Wang

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.236231Z

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Observation 5cfc9d13-1e43-416c-b3ec-473344322e59 · outbound

This paper cites Machine unlearning via null space cali- bration.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Machine unlearning via null space cali- bration

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.228421Z

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Observation 65a05efd-587b-4785-81b5-7f9c2e390156 · outbound

This paper cites Salun: Em- powering machine unlearning via gradient-based weight saliency in both image classification and generation.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Salun: Em- powering machine unlearning via gradient-based weight saliency in both image classification and generation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.210965Z

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Observation a3933796-3abf-4819-ab11-2543ced58c45 · outbound

This paper cites Model inversion attacks that exploit confidence information and basic countermeasures.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Model inversion attacks that exploit confidence information and basic countermeasures

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.202878Z

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Observation 9895ae72-dbd6-4d19-9ee5-41438352bc58 · outbound

This paper cites Eternal sunshine of the spotless net: Selective forgetting in deep networks.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Eternal sunshine of the spotless net: Selective forgetting in deep networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.194817Z

Source-reported events for the cited work

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Observation 55fb84a8-eb50-447d-b030-b319ff9285c8 · outbound

This paper cites Deep residual learning for image recog- nition.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Deep residual learning for image recog- nition

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.124999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9da33175-0e60-4997-b815-36232f0b0cc0 · outbound

This paper cites Learn what you want to unlearn: Un- learning inversion attacks against machine unlearning.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Learn what you want to unlearn: Un- learning inversion attacks against machine unlearning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.073331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88c05032-4b43-4fdf-bede-1234df0a1375 · outbound

This paper cites The significance and context of the establishment of california consumer privacy act of.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy The significance and context of the establishment of california consumer privacy act of

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.066229Z

Source-reported events for the cited work

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Observation 22d57a45-95eb-416f-adc0-8d6b674e5012 · outbound

This paper cites Model sparsity can simplify machine unlearning.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Model sparsity can simplify machine unlearning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.051106Z

Source-reported events for the cited work

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Observation 4a3842a5-66a5-44af-b099-ffda343113c2 · outbound

This paper cites Un- derstanding black-box predictions via influence functions.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Un- derstanding black-box predictions via influence functions

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.044120Z

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Observation c49e49b1-57f0-4daa-ae3f-8a827b7b4e42 · outbound

This paper cites Does label smoothing mitigate label noise? InInternational Con- ference on Machine Learning, pages 6448–6458.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Does label smoothing mitigate label noise? InInternational Con- ference on Machine Learning, pages 6448–6458

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.022369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6aff8be5-62a1-4f2b-9a9e-09f122afe657 · outbound

This paper cites Marchant, Benjamin I.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Marchant, Benjamin I

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.014746Z

Source-reported events for the cited work

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Observation 04e89144-f45e-48fb-a5bd-9a6a0ec3121f · outbound

This paper cites Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:12:35.910077Z

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Observation 38dbf931-e453-419f-ac5f-f8633546bc51 · outbound

This paper cites Automated flower classification over a large number of classes.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Automated flower classification over a large number of classes

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.004375Z

Source-reported events for the cited work

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Observation 4a1ed15c-c65b-4f3e-9f64-abef9a585761 · outbound

This paper cites Efficient- net: Rethinking model scaling for convolutional neural networks.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Efficient- net: Rethinking model scaling for convolutional neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:35.980903Z

Source-reported events for the cited work

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Observation 3d415ff6-cdef-490e-bef7-a5e011e4410f · outbound

This paper cites Unrolling sgd: Understanding factors influencing machine unlearning.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Unrolling sgd: Understanding factors influencing machine unlearning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:35.972784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6af498c4-04fd-4321-9188-08182aea4aff · outbound

This paper cites Hydiscgan: A hybrid dis- tributed cgan for audio-visual privacy preservation in mul- timodal sentiment analysis.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Hydiscgan: A hybrid dis- tributed cgan for audio-visual privacy preservation in mul- timodal sentiment analysis

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:35.965424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 97acab18-91be-40cd-99a9-c9e75a8aae00 · outbound

This paper cites Dauphin, and David Lopez-Paz.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Dauphin, and David Lopez-Paz

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:35.958208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9ae1cc44-08d1-411f-97ba-6e12e2a04cc9 · outbound

This paper cites An MU method with more precise unlearning may lead to higher success rate of MRA to recall the forgotten class memberships.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy An MU method with more precise unlearning may lead to higher success rate of MRA to recall the forgotten class memberships

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:35.950932Z

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Observation f4239527-4575-4a18-afb2-88fae28ba23a · outbound

This paper cites Parkhi, Andrea Vedaldi, An- drew Zisserman, and C.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Parkhi, Andrea Vedaldi, An- drew Zisserman, and C

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:35.996413Z

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Observation 2208a7e2-9438-4584-a821-edb204aefefa · outbound

This paper cites Swin trans- former v2: Scaling up capacity and resolution.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Swin trans- former v2: Scaling up capacity and resolution

Reference 2009

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.029648Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation df5f96aa-4c36-4112-bb43-429218ab6913 · outbound

This paper cites Membership infer- ence attacks against machine learning models.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Membership infer- ence attacks against machine learning models

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:35.988981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 669276b2-ddfb-436b-968b-1548d71af9bd · outbound

This paper cites On mixup regularization.Journal of Machine Learning Research, 23(325):1–31,.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy On mixup regularization.Journal of Machine Learning Research, 23(325):1–31,

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.250366Z

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Observation 38785fc8-c7f5-4d02-a2cc-da2830b95eeb · outbound

This paper cites Zuiderveen Borgesius.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Zuiderveen Borgesius

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.106836Z

Source-reported events for the cited work

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Observation 39a68e19-c6f4-4bea-8328-605917128436 · outbound

This paper cites Learning multiple layers of features from tiny im- ages.Master’s Thesis,.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Learning multiple layers of features from tiny im- ages.Master’s Thesis,

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.037147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b85e6e0a-2d89-455a-95ac-14fe6572b8d2 · outbound

This paper cites Approximate data dele- tion from machine learning models.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Approximate data dele- tion from machine learning models

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.059147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 884fe2b6-c6ea-4c03-8c5d-c4d22b74d2d8 · outbound

This paper cites A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services

Reference 2019

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:12:35.921917Z

Source-reported events for the cited work

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Observation 9773a1f6-ecd2-45e2-9695-71defc3e62c7 · outbound

This paper cites Amnesiac machine learning.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Amnesiac machine learning

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.159280Z

Source-reported events for the cited work

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Observation 0f63873e-f1d8-4ece-9673-da3c5a10a536 · outbound

This paper cites Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast

Reference 2021

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Source-reported events for the cited work

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Observation 86362bf8-db46-46ca-a720-7c6f4f811d98 · outbound

This paper cites Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.267534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2bb71b2b-31f1-4a20-a282-49ac62acc737 · outbound

This paper cites Evaluating Machine Unlearning via Epistemic Uncertainty.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy Evaluating Machine Unlearning via Epistemic Uncertainty

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:35.309152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d7994ae-e960-4be0-bd71-c3cd613a05f5 · outbound

This paper cites [Diet al., 2022 ] Jimmy Z.

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy [Diet al., 2022 ] Jimmy Z

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.218634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.