Paper Citation Record · LEDGER

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data

As of 19 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2505.11825.

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

pith.paper-citation-record.v1
2505.11825 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:53:26.038689Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

38 of 38 outbound references displayed

verified exact2
verified fuzzy10
unresolved26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation da5ba79a-33d3-4522-9f3b-04bf68c8b79b · outbound

This paper cites an unresolved cited work.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Unresolved cited work

Reference 1

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Observation 40b9c7d0-8e4c-4670-a522-a238c3707d15 · outbound

This paper cites Weighted sums of certain dependent random variables.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Weighted sums of certain dependent random variables

Reference 2

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Observation d10701eb-5310-4cd8-b890-6011e655ca50 · outbound

This paper cites An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations

Reference 3

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Observation f59b8538-6568-480a-889a-4722421c4f7e · outbound

This paper cites Webvid-10m: A large-scale video-text dataset.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Webvid-10m: A large-scale video-text dataset

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:27.631973Z

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Observation b93c2408-3cb1-49a2-a2ba-7f88f212cb79 · outbound

This paper cites an unresolved cited work.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Unresolved cited work

Reference 5

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Observation 86f2090f-64ed-4ba1-a1a7-527c9d921a4f · outbound

This paper cites Probability and Measure.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Probability and Measure

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:27.489127Z

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Observation 3e599c75-d1bc-4aba-8bca-f3e30e7f26b9 · outbound

This paper cites Coyo-700m: Image-text dataset for better text-to-image generation.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Coyo-700m: Image-text dataset for better text-to-image generation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:27.478075Z

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Observation 4d63c34b-8ad3-4f41-bc18-3f4bee18c48a · outbound

This paper cites Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation

Reference 8

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Observation 54d275b4-18d0-4e89-8b4f-f6e14c097d5a · outbound

This paper cites SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddc90e95-a73d-4635-9114-008490d6325a · outbound

This paper cites Stargan v2: Diverse image synthesis for multiple domains.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Stargan v2: Diverse image synthesis for multiple domains

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:27.224758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 95f3b144-0ed2-41fb-b4a8-fb0ca2def920 · outbound

This paper cites Modelscope text-to-video synthesis.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Modelscope text-to-video synthesis

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:26.929654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1f7cfcd1-1389-4fd7-a548-6ebc72732232 · outbound

This paper cites Ambient Diffusion: Learning Clean Distributions from Corrupted Data.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Ambient Diffusion: Learning Clean Distributions from Corrupted Data

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T20:53:25.293080Z

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Observation 8f1d16d9-e406-4d29-a4c2-c82ceadfcdf6 · outbound

This paper cites Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T20:53:25.296937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48d494ec-9729-4bb1-b819-294fab354f1f · outbound

This paper cites Diffusion Models Beat GANs on Image Synthesis.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Diffusion Models Beat GANs on Image Synthesis

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:53:25.300694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24393ec5-e334-490b-b9cd-49e1e92a2578 · outbound

This paper cites an unresolved cited work.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Unresolved cited work

Reference 15

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ce29af08-04f9-4534-86eb-9ee2d792fc26 · outbound

This paper cites Tweedie’s formula and selection bias.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Tweedie’s formula and selection bias

Reference 16

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Source-reported events for the cited work

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Observation 939156fd-c0e8-4514-a3c8-17599695d680 · outbound

This paper cites MINDE : Mutual information neural diffusion estimation.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data MINDE : Mutual information neural diffusion estimation

Reference 17

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Source-reported events for the cited work

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Observation 16036120-f588-4f58-9fb3-aa6d0ebc1863 · outbound

This paper cites On measuring excess capacity in neural networks.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data On measuring excess capacity in neural networks

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 083688d1-2334-4745-9720-600ea37f3d00 · outbound

This paper cites AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 19

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Observation 6887812c-f1f3-4745-8422-c1a750d2b983 · outbound

This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 20

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Source-reported events for the cited work

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Observation 4afd0db3-4986-4fda-b39e-5888d10b9f6c · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Denoising Diffusion Probabilistic Models

Reference 21

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Source-reported events for the cited work

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Observation 6a697472-ad21-4206-9fb9-e30dee7f3ad0 · outbound

This paper cites Video Diffusion Models.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Video Diffusion Models

Reference 22

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Source-reported events for the cited work

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Observation 11fb1fb5-0810-47bd-9769-d871a9906147 · outbound

This paper cites Elucidating the Design Space of Diffusion-Based Generative Models.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Elucidating the Design Space of Diffusion-Based Generative Models

Reference 23

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Observation 628aa308-6636-47e7-bb95-22fa44fb1796 · outbound

This paper cites GSURE-Based Diffusion Model Training with Corrupted Data.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data GSURE-Based Diffusion Model Training with Corrupted Data

Reference 24

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Source-reported events for the cited work

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Observation 5b0297ca-1966-416f-ba15-fb13af3a232e · outbound

This paper cites Scaling laws for diffusion transformers.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Scaling laws for diffusion transformers

Reference 25

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Source-reported events for the cited work

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Observation 20ea443d-1e22-4cb3-9987-9ee314f1181b · outbound

This paper cites Magic3D: High-Resolution Text-to-3D Content Creation.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Magic3D: High-Resolution Text-to-3D Content Creation

Reference 26

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Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data and Upfal, E

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:26.664230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e21e93f4-51bf-4439-a6b6-1e8bc01da845 · outbound

This paper cites Sora: World-modeling text-to-video generation.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Sora: World-modeling text-to-video generation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:26.623951Z

Source-reported events for the cited work

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Observation f50f1379-84ab-4471-8f1b-737723a55af5 · outbound

This paper cites DreamFusion: Text-to-3D using 2D Diffusion.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data DreamFusion: Text-to-3D using 2D Diffusion

Reference 29

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Observation a921e936-d0ae-45d0-a308-0bfbbf4bb2fb · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data High-Resolution Image Synthesis with Latent Diffusion Models

Reference 30

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Source-reported events for the cited work

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Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data LAION-5B: An open large-scale dataset for training next generation image-text models

Reference 31

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Observation 391ba653-15fc-43bf-ae42-358db6bb8901 · outbound

This paper cites Deep Unsupervised Learning using Nonequilibrium Thermodynamics.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Reference 32

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Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Score-Based Generative Modeling through Stochastic Differential Equations

Reference 33

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Source-reported events for the cited work

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Observation 33100fc6-3ddb-449e-94f2-18c6b8e398fd · outbound

This paper cites Versatile Diffusion: Text, Images and Variations All in One Diffusion Model.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Versatile Diffusion: Text, Images and Variations All in One Diffusion Model

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T20:53:25.846415Z

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Unavailable: canonical work link unavailable.

Observation 53fbe5a7-6fdc-4041-8173-c97178aaab83 · outbound

This paper cites A Little Book of Martingales, volume 78 of Texts and Readings in Mathematics.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data A Little Book of Martingales, volume 78 of Texts and Readings in Mathematics

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:53:26.534028Z

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Observation d20c9373-b9ba-40fe-b174-675a4a0438f3 · outbound

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Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:53:26.471991Z

Source-reported events for the cited work

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Observation abfda3bf-a30b-42f7-b7b2-c64180a59dde · outbound

This paper cites 3D Shape Generation and Completion through Point-Voxel Diffusion.

Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data 3D Shape Generation and Completion through Point-Voxel Diffusion

Reference 37

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Observation fb529d8f-dfdd-4089-bb74-1a7c8a5598eb · outbound

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Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data write newline

Reference 38

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Pith citing papers

No inbound Pith citation observations are available.