Paper Citation Record · LEDGER

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data

As of 15 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2501.19094.

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

pith.paper-citation-record.v1
2501.19094 v2

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:25:15.453950Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

20 of 20 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 31ddbb4c-126c-4ae0-bf16-6e88734dffe4 · outbound

This paper cites an unresolved cited work.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-09T21:25:15.715535Z

Source-reported events for the cited work

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Observation d9e322bf-7c88-4e83-a66f-67ffe219cc54 · outbound

This paper cites Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods,.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.706055Z

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Observation b25425f1-23e5-44e0-8a76-26641e290ea9 · outbound

This paper cites Approximating the ideal observer for joint signal detection and localization tasks by use of supervised learning methods,.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Approximating the ideal observer for joint signal detection and localization tasks by use of supervised learning methods,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.695172Z

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Observation b8593279-fa24-4619-87f2-baac05529066 · outbound

This paper cites Ideal observer computation by use of markov-chain monte carlo with generative adversarial networks,.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Ideal observer computation by use of markov-chain monte carlo with generative adversarial networks,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.684050Z

Source-reported events for the cited work

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Observation 7de09e6c-747b-4d2b-bf29-bd24982dcd17 · outbound

This paper cites An ideal observer for a model of x-ray imaging in breast parenchymal tissue,.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data An ideal observer for a model of x-ray imaging in breast parenchymal tissue,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.673736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 383c85a9-56a0-4c30-be68-9a2d15800a01 · outbound

This paper cites Effect of random background inhomogeneity on observer detection performance,.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Effect of random background inhomogeneity on observer detection performance,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.663393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7152a13e-9467-4504-b9b6-d1f237e2eaf7 · outbound

This paper cites Experimental determination of object statistics from noisy images,.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Experimental determination of object statistics from noisy images,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.653190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b3678b1a-5257-406c-b7e1-bbbc507d9748 · outbound

This paper cites Ambientgan: Generative models from lossy measurements,.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Ambientgan: Generative models from lossy measurements,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.643513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6d2508bf-b681-4081-a1a5-5ed8fabdb2eb · outbound

This paper cites Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks,.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.632680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 15a6b2f7-61dd-45fc-ac7a-6502b7afc1be · outbound

This paper cites Ambientcyclegan for establishing interpretable stochastic object models based on mathematical phantoms and medical imaging measurements,.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Ambientcyclegan for establishing interpretable stochastic object models based on mathematical phantoms and medical imaging measurements,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.621090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1a233bbc-e2fa-4e1f-a2c1-d4f5ab933448 · outbound

This paper cites Ambient-pix2pixgan for translating medical images from noisy data,.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Ambient-pix2pixgan for translating medical images from noisy data,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.610379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d6158518-80ec-48ff-bb76-89477b71b15a · outbound

This paper cites Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context,.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.598825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e58caeff-c75b-43cf-a4e4-fcbb096160ea · outbound

This paper cites Tackling the Generative Learning Trilemma with Denoising Diffusion GANs.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 13

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Source-reported events for the cited work

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Observation 864166e7-704f-4b48-b9ae-531e43d22dfe · outbound

This paper cites Denoising diffusion probabilistic models,.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Denoising diffusion probabilistic models,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.586199Z

Source-reported events for the cited work

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Observation 97283974-3721-4d5e-9edf-e1f27695fd2c · outbound

This paper cites Books and publications:.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Books and publications:

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.573664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ea13f8c9-6319-4346-9f7d-68aeda3cc77a · outbound

This paper cites Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning,.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.561564Z

Source-reported events for the cited work

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Observation 94a102d1-aefa-4ebb-94bc-900862ef7536 · outbound

This paper cites TorchRadon: Fast Differentiable Routines for Computed Tomography.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data TorchRadon: Fast Differentiable Routines for Computed Tomography

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T21:25:15.442285Z

Source-reported events for the cited work

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Observation 7b7f64f7-4bf6-4220-8e12-113173c58838 · outbound

This paper cites Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-09T21:25:15.512136Z

Source-reported events for the cited work

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Observation e226f28e-987c-4b56-b020-4cc83ea83dc7 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Adam: A Method for Stochastic Optimization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T21:25:15.449927Z

Source-reported events for the cited work

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Observation 537c423f-07c8-4f9b-b04a-7e8de7d36223 · outbound

This paper cites Alias-freegenerative adversarial networks,.

Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data Alias-freegenerative adversarial networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:25:15.546431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.