Paper Citation Record · LEDGER

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention

As of 9 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2502.04260.

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

pith.paper-citation-record.v1
2502.04260 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T23:04:31.425837Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

36 of 36 outbound references displayed

verified exact0
verified fuzzy14
unresolved18
parse uncertain0
malformed identifier1
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 65ba2fe8-374d-44f9-a236-b778e72eeb2b · outbound

This paper cites Advances in neural information processing systems 34, 8780–8794 (2021).

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Advances in neural information processing systems 34, 8780–8794 (2021)

Reference 1

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Source-reported events for the cited work

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Observation 09de1705-5de5-404a-8fe9-8273a6495cae · outbound

This paper cites GPT-4 Technical Report.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention GPT-4 Technical Report

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39cc6201-23de-4391-94a3-ac2db7c9d17c · outbound

This paper cites In: 2015 IEEE Symposium on Security and Privacy, pp.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: 2015 IEEE Symposium on Security and Privacy, pp

Reference 3

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Source-reported events for the cited work

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Observation 308b0962-d914-4706-9caa-8bf214d281b5 · outbound

This paper cites In: 2021 IEEE Symposium on Security and Privacy (SP), pp.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: 2021 IEEE Symposium on Security and Privacy (SP), pp

Reference 4

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Observation e4c8cb40-a38c-4263-ae8a-14d8eb455fc8 · outbound

This paper cites IEEE Transactions on Neural Networks and Learning Systems (2023).

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention IEEE Transactions on Neural Networks and Learning Systems (2023)

Reference 5

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Observation 229c6ab1-8505-4a07-a682-cdc304d64ad9 · outbound

This paper cites In: Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 6

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Observation 6b77fe29-fd6f-4d7f-b61c-66ab6432ed15 · outbound

This paper cites In: 32nd USENIX Security Symposium (USENIX Security 23), pp.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: 32nd USENIX Security Symposium (USENIX Security 23), pp

Reference 7

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Observation 112ceeb6-1d2f-4eaf-9e77-b182b8dc74c6 · outbound

This paper cites Machine Unlearning for Image-to-Image Generative Models.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Machine Unlearning for Image-to-Image Generative Models

Reference 8

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This paper cites Controllable Unlearning for Image-to-Image Generative Models via $\varepsilon$-Constrained Optimization.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Controllable Unlearning for Image-to-Image Generative Models via $\varepsilon$-Constrained Optimization

Reference 9

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Observation c5ff672a-f443-4911-a8bd-748db3a2eb37 · outbound

This paper cites Federated Unlearning: How to Efficiently Erase a Client in FL?.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 10

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Source-reported events for the cited work

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Observation 28992184-cc17-456e-9636-c14c35513a77 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:04:31.910745Z

Source-reported events for the cited work

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Observation cebd1905-d862-49d0-8778-0046feac80b2 · outbound

This paper cites In: ACM SIGGRAPH 2022 Conference Proceedings, pp.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: ACM SIGGRAPH 2022 Conference Proceedings, pp

Reference 12

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Observation 7ae92de5-4b80-4f74-a557-9acb0b3c991a · outbound

This paper cites In: Proceedings of the European Conference on Computer Vision (ECCV), pp.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: Proceedings of the European Conference on Computer Vision (ECCV), pp

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:04:31.884554Z

Source-reported events for the cited work

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Observation 39fd8c9c-f8ee-47ce-a14e-b897316eeeda · outbound

This paper cites In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:04:31.869122Z

Source-reported events for the cited work

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Observation a28332b2-d259-4cd3-b868-93177046f6ba · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:04:31.853347Z

Source-reported events for the cited work

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Observation b94f3085-051f-4a2d-8091-466950c0ce73 · outbound

This paper cites The Journal of Machine Learning Research 15(1), 20 3563–3593 (2014).

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention The Journal of Machine Learning Research 15(1), 20 3563–3593 (2014)

Reference 16

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Observation 55907fc7-fddd-4265-88e5-ade37c64e55c · outbound

This paper cites Communications of the ACM 63(11), 139–144 (2020).

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Communications of the ACM 63(11), 139–144 (2020)

Reference 17

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Observation 491a7eb5-de79-4475-93c7-a9158868801f · outbound

This paper cites Advances in neural information processing systems 33, 6840–6851 (2020).

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Advances in neural information processing systems 33, 6840–6851 (2020)

Reference 18

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 19

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Source-reported events for the cited work

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Observation add93c0c-1484-4c7a-8b0c-2ef71cd77b38 · outbound

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Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Certified Data Removal from Machine Learning Models

Reference 20

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Observation 675e9f1f-e928-4b10-b5bc-d4a7e17ed17e · outbound

This paper cites In: International Conference on Machine Learning, pp.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: International Conference on Machine Learning, pp

Reference 21

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Observation ad8b558b-1718-4c78-b3cc-7ef287161723 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 22

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Observation 6cf160f4-b310-489d-a32d-86efe4fa58cf · outbound

This paper cites Machine Unlearning in Generative AI: A Survey.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Machine Unlearning in Generative AI: A Survey

Reference 23

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Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Gradient Surgery for One-shot Unlearning on Generative Model

Reference 24

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Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Feature Unlearning for Pre-trained GANs and VAEs

Reference 25

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Observation 12187c21-d073-4be1-806a-89b87555e3ea · outbound

This paper cites In: International Conference on Machine Learning, pp.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: International Conference on Machine Learning, pp

Reference 26

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Observation a415a61c-9200-48e3-8d64-9a010ed3ef62 · outbound

This paper cites Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions

Reference 27

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Observation 69bf17db-0897-4441-9a86-d5bf89ecb534 · outbound

This paper cites Convergence of denoising diffusion models under the manifold hypothesis.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Convergence of denoising diffusion models under the manifold hypothesis

Reference 28

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Observation 27cd6aac-e215-47d3-bf11-8a4f5b025b41 · outbound

This paper cites Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization

Reference 29

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Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Unresolved cited work

Reference 30

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Observation 71adbd39-1718-493a-9a96-46885990b696 · outbound

This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Reference 31

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Observation 64ccb68f-83df-4a65-a7c2-42e7397c7c40 · outbound

This paper cites Advances in neural information processing systems 29 (2016).

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Advances in neural information processing systems 29 (2016)

Reference 32

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This paper cites Advances in neural information processing systems 30 (2017).

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Advances in neural information processing systems 30 (2017)

Reference 33

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Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention In: International Conference on Machine Learning, pp

Reference 34

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Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention 8717–8730 (2023)

Reference 35

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Observation e7ba522e-af4c-4167-bd49-1756ef14692d · outbound

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Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Unresolved cited work

Reference 36

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Pith citing papers

No inbound Pith citation observations are available.