Paper Citation Record · LEDGER

Memorization and Regularization in Generative Diffusion Models

As of 14 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 9 inbound Pith citation observations for arXiv:2501.15785.

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

pith.paper-citation-record.v1
2501.15785 v2

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

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measured 67 of 67 standing notices

One-hop event checks from named stored sources.

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measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T04:53:14.333220Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T23:57:28.827531Z

Reference resolution

58 of 58 outbound references displayed

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External citation measurements

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Outbound references

Observation 15457dc0-d563-484d-8da1-575819d8cf8d · outbound

This paper cites Understanding Hallucinations in Diffusion Models through Mode Interpolation.

Memorization and Regularization in Generative Diffusion Models Understanding Hallucinations in Diffusion Models through Mode Interpolation

Reference 1

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Observation 8e0410a1-b54f-4280-8274-b87554914b89 · outbound

This paper cites Reverse-time diffusion equation models.

Memorization and Regularization in Generative Diffusion Models Reverse-time diffusion equation models

Reference 2

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Observation 3f488980-8b2a-449e-9cbb-ea7e8c915897 · outbound

This paper cites Reducing Training Sample Memorization in GANs by Training with Memorization Rejection.

Memorization and Regularization in Generative Diffusion Models Reducing Training Sample Memorization in GANs by Training with Memorization Rejection

Reference 3

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Observation 3c62326d-79f3-44b4-bb59-f784cdad7bfb · outbound

This paper cites Flow map matching with stochastic interpolants: A mathematical framework for consistency models.

Memorization and Regularization in Generative Diffusion Models Flow map matching with stochastic interpolants: A mathematical framework for consistency models

Reference 4

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Observation 188a0c1f-6806-4166-acce-5316abd5d0a2 · outbound

This paper cites Extracting training data from large language models.

Memorization and Regularization in Generative Diffusion Models Extracting training data from large language models

Reference 5

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Observation 00c48741-4dda-4dbd-9186-dd934f28c78e · outbound

This paper cites Extracting training data from diffusion models.

Memorization and Regularization in Generative Diffusion Models Extracting training data from diffusion models

Reference 6

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Observation 8dc7d303-c29e-45d1-9fe3-0aade1cedfa8 · outbound

This paper cites Towards memorization-free diffusion models.

Memorization and Regularization in Generative Diffusion Models Towards memorization-free diffusion models

Reference 7

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Observation 324cf228-ae41-4a0b-8d3f-f48e135b4b48 · outbound

This paper cites Investigating Memorization in Video Diffusion Models.

Memorization and Regularization in Generative Diffusion Models Investigating Memorization in Video Diffusion Models

Reference 8

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Observation d85c5efb-ac2e-46b9-9d36-497298d1e1e9 · outbound

This paper cites Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions.

Memorization and Regularization in Generative Diffusion Models Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 9

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Observation 6d3ce6f7-d646-4a4d-8023-6cd902bf53bd · outbound

This paper cites SIDE: Surrogate Conditional Data Extraction from Diffusion Models.

Memorization and Regularization in Generative Diffusion Models SIDE: Surrogate Conditional Data Extraction from Diffusion Models

Reference 10

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Observation 6c6cf57c-63b4-4061-a4b6-99a492c1fd14 · outbound

This paper cites CogMol: Target-specific and selective drug design for COVID-19 using deep generative models.

Memorization and Regularization in Generative Diffusion Models CogMol: Target-specific and selective drug design for COVID-19 using deep generative models

Reference 11

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Observation deddcaf2-d02b-43b9-99f8-c0000d963c62 · outbound

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

Memorization and Regularization in Generative Diffusion Models Convergence of denoising diffusion models under the manifold hypothesis

Reference 12

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Observation d3c55edb-b372-4ae9-8060-775658ad33ed · outbound

This paper cites Diffusion models beat GANs on image synthesis.

Memorization and Regularization in Generative Diffusion Models Diffusion models beat GANs on image synthesis

Reference 13

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Observation 2fb9b0ce-1916-49b0-bc37-873c17b56bc1 · outbound

This paper cites Are diffusion models vulnerable to membership inference attacks? In International Conference on Machine Learning, pages 8717–8730.

Memorization and Regularization in Generative Diffusion Models Are diffusion models vulnerable to membership inference attacks? In International Conference on Machine Learning, pages 8717–8730

Reference 14

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Observation f1caa46b-919e-4cf8-a3c9-4237f7867b54 · outbound

This paper cites Capacity Control is an Effective Memorization Mitigation Mechanism in Text-Conditional Diffusion Models.

Memorization and Regularization in Generative Diffusion Models Capacity Control is an Effective Memorization Mitigation Mechanism in Text-Conditional Diffusion Models

Reference 15

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Observation f365fd1b-e5fc-43aa-acb0-0acc13598b90 · outbound

This paper cites On Memorization in Diffusion Models.

Memorization and Regularization in Generative Diffusion Models On Memorization in Diffusion Models

Reference 16

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Observation cda926c2-5b33-4679-882d-e0821244e510 · outbound

This paper cites Ordinary differential equations.

Memorization and Regularization in Generative Diffusion Models Ordinary differential equations

Reference 17

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Observation 31005388-b9b7-400d-b1ad-dbbecd6ed1f5 · outbound

This paper cites Time reversal of diffusions.

Memorization and Regularization in Generative Diffusion Models Time reversal of diffusions

Reference 18

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Observation 6f255c35-4a91-47c5-a638-9f23424b2445 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

Memorization and Regularization in Generative Diffusion Models Elucidating the design space of diffusion-based generative models

Reference 19

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Observation 6bcf412c-6191-48ca-b4f9-74cce4a60111 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Memorization and Regularization in Generative Diffusion Models Adam: A Method for Stochastic Optimization

Reference 20

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Observation 00eefacf-41ac-4200-8c7e-5e887c20cc66 · outbound

This paper cites DiffWave: A Versatile Diffusion Model for Audio Synthesis.

Memorization and Regularization in Generative Diffusion Models DiffWave: A Versatile Diffusion Model for Audio Synthesis

Reference 21

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This paper cites A Good Score Does not Lead to A Good Generative Model.

Memorization and Regularization in Generative Diffusion Models A Good Score Does not Lead to A Good Generative Model

Reference 22

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Observation 4d04585d-1571-4566-b1da-7d081a7036c6 · outbound

This paper cites Mathematical analysis of singularities in the diffusion model under the submanifold assumption.

Memorization and Regularization in Generative Diffusion Models Mathematical analysis of singularities in the diffusion model under the submanifold assumption

Reference 23

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Observation 5643acc3-ea45-47ab-89bf-0780fa206309 · outbound

This paper cites An Inversion-based Measure of Memorization for Diffusion Models.

Memorization and Regularization in Generative Diffusion Models An Inversion-based Measure of Memorization for Diffusion Models

Reference 24

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Observation 29c02e5f-9bd1-44ba-be07-ec5f27d0e167 · outbound

This paper cites Stochastic differential equations and applications.

Memorization and Regularization in Generative Diffusion Models Stochastic differential equations and applications

Reference 25

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This paper cites Theoretical insights into memorization in GANs.

Memorization and Regularization in Generative Diffusion Models Theoretical insights into memorization in GANs

Reference 26

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This paper cites Stochastic processes and applications.

Memorization and Regularization in Generative Diffusion Models Stochastic processes and applications

Reference 27

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Observation 54cd2df2-efeb-48cc-ac27-894f7513a772 · outbound

This paper cites Score-based generative models detect manifolds.

Memorization and Regularization in Generative Diffusion Models Score-based generative models detect manifolds

Reference 28

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Observation c9d2b2b8-754f-46cc-8c03-ab77989d5570 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Memorization and Regularization in Generative Diffusion Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 29

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Observation 9bf95cef-d2c2-4a62-aa44-84d9828af7f5 · outbound

This paper cites Skilful precipitation nowcasting using deep generative models of radar.Nature, 597(7878):672–677, 2021.

Memorization and Regularization in Generative Diffusion Models Skilful precipitation nowcasting using deep generative models of radar.Nature, 597(7878):672–677, 2021

Reference 30

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Observation f149899c-30e5-4156-9d79-e672d16293dd · outbound

This paper cites Unveiling and mitigating memorization in text-to-image diffusion models through cross attention.

Memorization and Regularization in Generative Diffusion Models Unveiling and mitigating memorization in text-to-image diffusion models through cross attention

Reference 31

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Observation 7ca2bec0-a064-463e-9fff-3f51dfe00359 · outbound

This paper cites Diffusions, markov processes, and martingales: Volume 1, foundations.

Memorization and Regularization in Generative Diffusion Models Diffusions, markov processes, and martingales: Volume 1, foundations

Reference 32

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This paper cites High-resolution image synthesis with latent diffusion models.

Memorization and Regularization in Generative Diffusion Models High-resolution image synthesis with latent diffusion models

Reference 33

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Observation 8df6a1ab-be3d-41b7-8c62-1d0548acbddc · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject- driven generation.

Memorization and Regularization in Generative Diffusion Models Dreambooth: Fine tuning text-to-image diffusion models for subject- driven generation

Reference 34

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Observation 52d7b984-bb7b-4a86-aa10-37f884dabb44 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

Memorization and Regularization in Generative Diffusion Models Photorealistic text-to-image diffusion models with deep language understanding

Reference 35

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Observation bbd7ff5a-8bb4-48f5-8937-3193276f18b5 · outbound

This paper cites Closed-Form Diffusion Models.

Memorization and Regularization in Generative Diffusion Models Closed-Form Diffusion Models

Reference 36

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This paper cites Weak and strong uniform consistency of the kernel estimate of a density and its derivatives.

Memorization and Regularization in Generative Diffusion Models Weak and strong uniform consistency of the kernel estimate of a density and its derivatives

Reference 37

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Observation ba9d4372-d1aa-47a3-898f-0d892f821c97 · outbound

This paper cites Density estimation for statistics and data analysis.

Memorization and Regularization in Generative Diffusion Models Density estimation for statistics and data analysis

Reference 38

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Observation 296e0e5a-ebb4-49a0-88a4-806f202d5dbe · outbound

This paper cites Diffusion art or digital forgery? Investigating data replication in diffusion models.

Memorization and Regularization in Generative Diffusion Models Diffusion art or digital forgery? Investigating data replication in diffusion models

Reference 39

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This paper cites Understanding and mitigating copying in diffusion models.

Memorization and Regularization in Generative Diffusion Models Understanding and mitigating copying in diffusion models

Reference 40

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This paper cites Maximum likelihood training of score-based diffusion models.Advances in Neural Information Processing Systems, 34:1415–1428, 2021.

Memorization and Regularization in Generative Diffusion Models Maximum likelihood training of score-based diffusion models.Advances in Neural Information Processing Systems, 34:1415–1428, 2021

Reference 41

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This paper cites Score-based generative modeling through stochastic differential equations.

Memorization and Regularization in Generative Diffusion Models Score-based generative modeling through stochastic differential equations

Reference 42

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This paper cites Elucidating Flow Matching ODE Dynamics with Respect to Data Geometries and Denoisers.

Memorization and Regularization in Generative Diffusion Models Elucidating Flow Matching ODE Dynamics with Respect to Data Geometries and Denoisers

Reference 43

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Observation efb8a8a6-0a87-4c14-9103-417ad34f1218 · outbound

This paper cites Detecting, explaining, and mitigating memorization in diffusion models.

Memorization and Regularization in Generative Diffusion Models Detecting, explaining, and mitigating memorization in diffusion models

Reference 44

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This paper cites Optimal score estimation via empirical Bayes smoothing.

Memorization and Regularization in Generative Diffusion Models Optimal score estimation via empirical Bayes smoothing

Reference 45

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This paper cites Diffusion probabilistic models generalize when they fail to memorize.

Memorization and Regularization in Generative Diffusion Models Diffusion probabilistic models generalize when they fail to memorize

Reference 46

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This paper cites Wasserstein proximal operators describe score-based generative models and resolve memorization.

Memorization and Regularization in Generative Diffusion Models Wasserstein proximal operators describe score-based generative models and resolve memorization

Reference 47

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This paper cites The emergence of reproducibility and consistency in diffusion models.

Memorization and Regularization in Generative Diffusion Models The emergence of reproducibility and consistency in diffusion models

Reference 48

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Memorization and Regularization in Generative Diffusion Models Unresolved cited work

Reference 49

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Observation c8296c25-0271-4127-bdb6-982bc7e7963f · outbound

This paper cites strictly negative) iff x is in the set containing xn 0 (resp.

Memorization and Regularization in Generative Diffusion Models strictly negative) iff x is in the set containing xn 0 (resp

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:41.429280Z

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This paper cites Hence, the normalized weights satisfy ωℓ(y, s) = eωℓ y, s PN ℓ=1 eωℓ y, s ≤ eωℓ y, s eω1 y, s < exp − e2s 2 δ2.

Memorization and Regularization in Generative Diffusion Models Hence, the normalized weights satisfy ωℓ(y, s) = eωℓ y, s PN ℓ=1 eωℓ y, s ≤ eωℓ y, s eω1 y, s < exp − e2s 2 δ2

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:41.417377Z

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Observation 3cd07694-67bf-4822-93d6-d68b349b38a4 · outbound

This paper cites (A.6) For the choice of s ≥ sα we have |yN (y, s) − x1 0| ≤2(N − 1) exp − e2s0 2 δ2 |x|∞ < α 2D+.

Memorization and Regularization in Generative Diffusion Models (A.6) For the choice of s ≥ sα we have |yN (y, s) − x1 0| ≤2(N − 1) exp − e2s0 2 δ2 |x|∞ < α 2D+

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:41.403737Z

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Observation 60653755-b61a-4397-b60a-5eb4e7e3b1df · outbound

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Memorization and Regularization in Generative Diffusion Models Unresolved cited work

Reference 53

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Observation 2c1c27d0-062a-43b1-b53f-6abffa174278 · outbound

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Memorization and Regularization in Generative Diffusion Models Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:00:41.376871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a25efdf1-7d7f-490b-8708-5d058966e682 · outbound

This paper cites an unresolved cited work.

Memorization and Regularization in Generative Diffusion Models Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:00:41.365666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f891753e-67cf-450f-9d99-e1668096e845 · outbound

This paper cites Define the error ϵN (y, s) := x1 0 − yN (y, s)√ 1 − e−2s = x1 0 − yN (y, s) + yN (y, s) − yN (y, s)√ 1 − e−2s.

Memorization and Regularization in Generative Diffusion Models Define the error ϵN (y, s) := x1 0 − yN (y, s)√ 1 − e−2s = x1 0 − yN (y, s) + yN (y, s) − yN (y, s)√ 1 − e−2s

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:41.354071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5900a291-572a-4305-a600-43a2dc3c67c8 · outbound

This paper cites (A.13) Moreover, for s ≥ s1 we have |x1 0 − yN (y, s)| ≤2(N − 1) exp − e2sα 4 δ2 |x|∞ < α1 2D+.

Memorization and Regularization in Generative Diffusion Models (A.13) Moreover, for s ≥ s1 we have |x1 0 − yN (y, s)| ≤2(N − 1) exp − e2sα 4 δ2 |x|∞ < α1 2D+

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:41.341775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 39e1a7a1-0855-49fc-9c35-c78d318574d9 · outbound

This paper cites Multiplying by the integrating factor es yields the formal solution y(s) − x1 0 = (y(s0) − x1 0)e−s+s0 + Z s s0 e−s+τ ϵN (y, τ)dτ.

Memorization and Regularization in Generative Diffusion Models Multiplying by the integrating factor es yields the formal solution y(s) − x1 0 = (y(s0) − x1 0)e−s+s0 + Z s s0 e−s+τ ϵN (y, τ)dτ

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:41.328358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 30f31c8b-790c-40ef-a763-4de7609effec · inbound

Taking a Big Step: Large Learning Rates in Denoising Score Matching Prevent Memorization cites this paper.

Taking a Big Step: Large Learning Rates in Denoising Score Matching Prevent Memorization Memorization and Regularization in Generative Diffusion Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T04:53:14.333220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fca02d5c-1b07-4330-bb39-5b442a48a99d · inbound

When and how can inexact generative models still sample from the data manifold? cites this paper.

When and how can inexact generative models still sample from the data manifold? Memorization and Regularization in Generative Diffusion Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T22:07:58.014891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df378c41-77ba-4a7f-b757-46565de89c38 · inbound

On The Hidden Biases of Flow Matching Samplers cites this paper.

On The Hidden Biases of Flow Matching Samplers Memorization and Regularization in Generative Diffusion Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-16T21:11:16.929065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 676be107-b330-4e8d-ac2a-b35d73a0eec1 · inbound

A Kinetic Energy Perspective of Flow Matching cites this paper.

A Kinetic Energy Perspective of Flow Matching Memorization and Regularization in Generative Diffusion Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T03:31:36.818911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43c60527-dfaf-4f1d-b17e-0475f907abce · inbound

Conditional flow matching for physics-constrained inverse problems with finite training data cites this paper.

Conditional flow matching for physics-constrained inverse problems with finite training data Memorization and Regularization in Generative Diffusion Models

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-15T10:59:57.381972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0fed71cf-5b2d-4864-8455-1202e1c7199d · inbound

On the Memorization of Consistency Distillation for Diffusion Models cites this paper.

On the Memorization of Consistency Distillation for Diffusion Models Memorization and Regularization in Generative Diffusion Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:11:11.008730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e385735f-4eae-4396-bce2-eacdadde45e6 · inbound

Tessellations of Semi-Discrete Flow Matching cites this paper.

Tessellations of Semi-Discrete Flow Matching Memorization and Regularization in Generative Diffusion Models

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:10:53.719942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 560f1f63-88d0-44a7-83bd-f5e09cb29abd · inbound

Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles cites this paper.

Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles Memorization and Regularization in Generative Diffusion Models

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:57:28.829434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 27b71681-948e-4fcf-b4ea-fda4f4a11e14 · inbound

PAC-DP: PAC-Bayesian Diffusion Policy Learning cites this paper.

PAC-DP: PAC-Bayesian Diffusion Policy Learning Memorization and Regularization in Generative Diffusion Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-31T18:58:09.295427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.