Diffusion Models Memorize in Training -- and Generalize in Inference | Paper Citation Record

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This paper cites arXiv preprint arXiv:2410.08727 , year=.

Diffusion Models Memorize in Training -- and Generalize in Inference arXiv preprint arXiv:2410.08727 , year=

Reference 1

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This paper cites Guiding a diffusion model using sliding windows.

Diffusion Models Memorize in Training -- and Generalize in Inference Guiding a diffusion model using sliding windows

Reference 2

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Observation eaf6bf94-4d38-4b43-b261-1af2df2bb771 · outbound

This paper cites Rethinking cluster-conditioned diffusion models for label-free image synthesis.

Diffusion Models Memorize in Training -- and Generalize in Inference Rethinking cluster-conditioned diffusion models for label-free image synthesis

Reference 3

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Observation b3c42993-02ec-4f07-935d-58179656a0f6 · outbound

This paper cites Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance.

Diffusion Models Memorize in Training -- and Generalize in Inference Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance

Reference 4

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Observation 3207ff2e-52f5-419a-801b-b3c2b6a1524e · outbound

This paper cites Nature Communications15(1) (Nov 2024).https://doi.org/10.1038/ s41467-024-54281-3,http://dx.doi.org/10.1038/s41467-024-54281-3.

Diffusion Models Memorize in Training -- and Generalize in Inference Nature Communications15(1) (Nov 2024).https://doi.org/10.1038/ s41467-024-54281-3,http://dx.doi.org/10.1038/s41467-024-54281-3

Reference 5

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Observation 3340802a-9236-4664-a6a6-59bf2696661f · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

Diffusion Models Memorize in Training -- and Generalize in Inference Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 6

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Observation 592e771a-b7ab-430d-a100-32f5d3ffdfed · outbound

This paper cites & Mézard, M.Why Diffusion Models Don’t Memorize: The Role of Implicit Dynamical Regularization in TrainingarXiv:2505.17638 [cs].

Diffusion Models Memorize in Training -- and Generalize in Inference & Mézard, M.Why Diffusion Models Don’t Memorize: The Role of Implicit Dynamical Regularization in TrainingarXiv:2505.17638 [cs]

Reference 7

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Observation 0bf5aa68-eec2-4d8b-9091-80f0e0a521c7 · outbound

This paper cites On the Edge of Memorization in Diffusion Models.

Diffusion Models Memorize in Training -- and Generalize in Inference On the Edge of Memorization in Diffusion Models

Reference 8

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Observation 2263f37d-a183-47af-90db-1190b7f573d2 · outbound

This paper cites On Memorization in Probabilistic Deep Generative Models.

Diffusion Models Memorize in Training -- and Generalize in Inference On Memorization in Probabilistic Deep Generative Models

Reference 9

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Observation cd70add8-0503-4ce2-9bb6-babab1a4db19 · outbound

This paper cites Extracting Training Data from Diffusion Models.

Diffusion Models Memorize in Training -- and Generalize in Inference Extracting Training Data from Diffusion Models

Reference 10

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Observation 77e31558-803d-4d2d-972d-eb51b3c2116a · outbound

This paper cites Exploring Local Memorization in Diffusion Models via Bright Ending Attention.

Diffusion Models Memorize in Training -- and Generalize in Inference Exploring Local Memorization in Diffusion Models via Bright Ending Attention

Reference 11

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This paper cites Effectively Unbiased FID and Inception Score and where to find them.

Diffusion Models Memorize in Training -- and Generalize in Inference Effectively Unbiased FID and Inception Score and where to find them

Reference 12

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Observation 5ef1290b-5a77-4aaa-ac72-bf4de8d8ae39 · outbound

This paper cites Nature Biomedical Engineering (2025) https://doi.org/ 10.1038/s41551-025-01468-8.

Diffusion Models Memorize in Training -- and Generalize in Inference Nature Biomedical Engineering (2025) https://doi.org/ 10.1038/s41551-025-01468-8

Reference 13

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Observation 1abe2382-9c44-4f67-89d7-c5479b0498a2 · outbound

This paper cites Diffusion Models Beat GANs on Image Synthesis.

Diffusion Models Memorize in Training -- and Generalize in Inference Diffusion Models Beat GANs on Image Synthesis

Reference 14

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Observation cccd3f03-40e5-481f-9fc6-297fd053dd0b · outbound

This paper cites Demystifying Foreground-Background Memorization in Diffusion Models.

Diffusion Models Memorize in Training -- and Generalize in Inference Demystifying Foreground-Background Memorization in Diffusion Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:54:07.918794Z

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Observation cbe3c3c1-2641-4f16-8f71-9dc2323e3bc6 · outbound

This paper cites The Devil is in the Prompts: De-Identification Traces Enhance Memorization Risks in Synthetic Chest X-Ray Generation.

Diffusion Models Memorize in Training -- and Generalize in Inference The Devil is in the Prompts: De-Identification Traces Enhance Memorization Risks in Synthetic Chest X-Ray Generation

Reference 16

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Observation 8f89234f-4186-4b39-bb23-9f4c4f7cd1fc · outbound

This paper cites MemControl: Mitigating Memorization in Diffusion Models via Automated Parameter Selection.

Diffusion Models Memorize in Training -- and Generalize in Inference MemControl: Mitigating Memorization in Diffusion Models via Automated Parameter Selection

Reference 17

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Observation 50c7a628-51d4-4794-ad78-664226c8041f · outbound

This paper cites Diffusion models and the manifold hypothesis: Log-domain smoothing is geometry adaptive.

Diffusion Models Memorize in Training -- and Generalize in Inference Diffusion models and the manifold hypothesis: Log-domain smoothing is geometry adaptive

Reference 18

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Observation c2fa3806-b403-474f-89d9-cc16a8f99564 · outbound

This paper cites Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis.

Diffusion Models Memorize in Training -- and Generalize in Inference Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis

Reference 19

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Observation 50f6ee56-c480-420a-a4ea-75226d8872f3 · outbound

This paper cites Testing the Manifold Hypothesis.

Diffusion Models Memorize in Training -- and Generalize in Inference Testing the Manifold Hypothesis

Reference 20

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Observation a1830376-886b-4c27-b137-3f7a53b8347e · outbound

This paper cites How Do Flow Matching Models Memorize and Generalize in Sample Data Subspaces?.

Diffusion Models Memorize in Training -- and Generalize in Inference How Do Flow Matching Models Memorize and Generalize in Sample Data Subspaces?

Reference 21

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Observation 34d0ced3-21a1-4312-8062-703ea274b56f · outbound

This paper cites Denoising Score Matching with Random Features: Insights on Diffusion Models from Precise Learning Curves.

Diffusion Models Memorize in Training -- and Generalize in Inference Denoising Score Matching with Random Features: Insights on Diffusion Models from Precise Learning Curves

Reference 22

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Observation 64e2570c-aefd-4c6f-8147-f3b06ad7009b · outbound

This paper cites On Memorization in Diffusion Models.

Diffusion Models Memorize in Training -- and Generalize in Inference On Memorization in Diffusion Models

Reference 23

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Observation eeaf3ab5-9c31-4501-a83f-8b6e79cdc05a · outbound

This paper cites In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G.

Diffusion Models Memorize in Training -- and Generalize in Inference In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G

Reference 24

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This paper cites A solvable generative model with a linear, one-step denoiser.

Diffusion Models Memorize in Training -- and Generalize in Inference A solvable generative model with a linear, one-step denoiser

Reference 25

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Observation 6be701fe-a305-4294-9d7e-8178e6aecc93 · outbound

This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.

Diffusion Models Memorize in Training -- and Generalize in Inference GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 26

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Diffusion Models Memorize in Training -- and Generalize in Inference Unresolved cited work

Reference 27

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Observation f161b3a3-b5dc-4455-aa22-66a2a45c5109 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Diffusion Models Memorize in Training -- and Generalize in Inference Classifier-Free Diffusion Guidance

Reference 28

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This paper cites an unresolved cited work.

Diffusion Models Memorize in Training -- and Generalize in Inference Unresolved cited work

Reference 29

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Observation 264ceeb4-074f-464a-b91b-ca25a7abc228 · outbound

This paper cites Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention.

Diffusion Models Memorize in Training -- and Generalize in Inference Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention

Reference 30

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Observation 5d485529-abfb-4c0c-8453-f9fd9b9aa3a0 · outbound

This paper cites IMPACT: Iterative Mask-based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling.

Diffusion Models Memorize in Training -- and Generalize in Inference IMPACT: Iterative Mask-based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling

Reference 31

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Observation 57bc67d6-2b79-47a4-8100-e00e957d52b9 · outbound

This paper cites Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models.

Diffusion Models Memorize in Training -- and Generalize in Inference Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models

Reference 32

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Observation d4bdb464-573e-489d-b734-4a83c49c31cb · outbound

This paper cites Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples.

Diffusion Models Memorize in Training -- and Generalize in Inference Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples

Reference 33

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This paper cites Generalization in diffusion models arises from geometry-adaptive harmonic representations.

Diffusion Models Memorize in Training -- and Generalize in Inference Generalization in diffusion models arises from geometry-adaptive harmonic representations

Reference 34

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Observation e968bcad-87f7-4717-80fe-e2f2124da6c6 · outbound

This paper cites An analytic theory of creativity in convolutional diffusion models.

Diffusion Models Memorize in Training -- and Generalize in Inference An analytic theory of creativity in convolutional diffusion models

Reference 35

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Observation 5d91950b-db25-44c8-8d0e-96892e460cbc · outbound

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

Diffusion Models Memorize in Training -- and Generalize in Inference Elucidating the Design Space of Diffusion-Based Generative Models

Reference 36

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This paper cites Guiding a Diffusion Model with a Bad Version of Itself.

Diffusion Models Memorize in Training -- and Generalize in Inference Guiding a Diffusion Model with a Bad Version of Itself

Reference 37

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Observation 892d8635-5ab3-4537-bf89-42fda1773b8f · outbound

This paper cites Analyzing and Improving the Training Dynamics of Diffusion Models.

Diffusion Models Memorize in Training -- and Generalize in Inference Analyzing and Improving the Training Dynamics of Diffusion Models

Reference 38

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This paper cites an unresolved cited work.

Diffusion Models Memorize in Training -- and Generalize in Inference Unresolved cited work

Reference 39

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Diffusion Models Memorize in Training -- and Generalize in Inference Unresolved cited work

Reference 40

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Observation bef02f8a-d550-4c20-ae24-4d3917963834 · outbound

This paper cites Finding DoRI: Discovery of Retained Images in Diffusion Models.

Diffusion Models Memorize in Training -- and Generalize in Inference Finding DoRI: Discovery of Retained Images in Diffusion Models

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-29T02:04:54.769376Z

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Observation d8e4dd18-5751-40fe-bcf0-56855f426025 · outbound

This paper cites AudioLDM: Text-to-Audio Generation with Latent Diffusion Models.

Diffusion Models Memorize in Training -- and Generalize in Inference AudioLDM: Text-to-Audio Generation with Latent Diffusion Models

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:54:07.907159Z

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Observation e4d929fa-93df-439c-9a13-0b1757c0de0e · outbound

This paper cites Locality in image diffusion models emerges from data statistics.

Diffusion Models Memorize in Training -- and Generalize in Inference Locality in image diffusion models emerges from data statistics

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:54:07.927715Z

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Observation 6134f40c-ed22-4750-b063-0da622dd201a · outbound

This paper cites Video Diffusion Models: A Survey.

Diffusion Models Memorize in Training -- and Generalize in Inference Video Diffusion Models: A Survey

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:54:07.916538Z

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Observation 9f6d3412-8ad2-4ed0-bb20-516067431f0c · outbound

This paper cites Private Synthetic Text Generation with Diffusion Models.

Diffusion Models Memorize in Training -- and Generalize in Inference Private Synthetic Text Generation with Diffusion Models

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:54:07.892093Z

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Observation a67bc087-c267-46a6-aa33-830ab19f6f8b · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Diffusion Models Memorize in Training -- and Generalize in Inference DINOv2: Learning Robust Visual Features without Supervision

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-05-21T10:54:07.903814Z

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Observation 0e2250ce-9caf-4fd1-a1a0-d2bf9d2dbaa7 · outbound

This paper cites an unresolved cited work.

Diffusion Models Memorize in Training -- and Generalize in Inference Unresolved cited work

Reference 47

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Observation 4b89b864-a8d3-4af4-b98e-ca5d083ed309 · outbound

This paper cites Unveiling and Mitigating Memorization in Text-to-image Diffusion Models through Cross Attention.

Diffusion Models Memorize in Training -- and Generalize in Inference Unveiling and Mitigating Memorization in Text-to-image Diffusion Models through Cross Attention

Reference 48

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Observation 4f3d3257-5429-4c19-aa5d-eb998df40cb3 · outbound

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

Diffusion Models Memorize in Training -- and Generalize in Inference High-Resolution Image Synthesis with Latent Diffusion Models

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-21T10:54:07.956262Z

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Observation c305036c-fd18-4a7f-8fc6-e6ca6078789b · outbound

This paper cites Closed-Form Diffusion Models.

Diffusion Models Memorize in Training -- and Generalize in Inference Closed-Form Diffusion Models

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:54:07.949189Z

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Observation 9711308c-6c24-45c8-9787-d78d74ea6792 · outbound

This paper cites org/abs/2502.21278.

Diffusion Models Memorize in Training -- and Generalize in Inference org/abs/2502.21278

Reference 51

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Diffusion Models Memorize in Training -- and Generalize in Inference Unresolved cited work

Reference 52

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This paper cites Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models.

Diffusion Models Memorize in Training -- and Generalize in Inference Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models

Reference 53

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This paper cites an unresolved cited work.

Diffusion Models Memorize in Training -- and Generalize in Inference Unresolved cited work

Reference 54

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This paper cites Generative Modeling by Estimating Gradients of the Data Distribution.

Diffusion Models Memorize in Training -- and Generalize in Inference Generative Modeling by Estimating Gradients of the Data Distribution

Reference 55

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Diffusion Models Memorize in Training -- and Generalize in Inference Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-05-21T10:54:08.291567Z

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This paper cites Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models.

Diffusion Models Memorize in Training -- and Generalize in Inference Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:54:07.870843Z

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Observation ce75045c-b4ce-4203-8b54-4c5cdb563c34 · outbound

This paper cites A note on the evaluation of generative models.

Diffusion Models Memorize in Training -- and Generalize in Inference A note on the evaluation of generative models

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-05-21T10:54:07.960941Z

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Observation b55b5e8c-517a-4227-9394-5c2caf09ea36 · outbound

This paper cites Manifolds, Random Matrices and Spectral Gaps: The geometric phases of generative diffusion.

Diffusion Models Memorize in Training -- and Generalize in Inference Manifolds, Random Matrices and Spectral Gaps: The geometric phases of generative diffusion

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:54:07.910932Z

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Observation aed1d325-c14b-40f6-ac9a-77c1fa19ec6f · outbound

This paper cites LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models.

Diffusion Models Memorize in Training -- and Generalize in Inference LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:54:07.916209Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b3e48828-8c16-49c9-b6c6-70b1bdb618d6 · outbound

This paper cites Energy-Based Diffusion Language Models for Text Generation.

Diffusion Models Memorize in Training -- and Generalize in Inference Energy-Based Diffusion Language Models for Text Generation

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:54:07.924812Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1850232d-3c9b-430e-9ad2-d78bfd24506d · outbound

This paper cites an unresolved cited work.

Diffusion Models Memorize in Training -- and Generalize in Inference Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-05-21T10:54:08.288758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 77e9f3f5-0644-48ef-9c3e-fb5d22623345 · outbound

This paper cites PeerJ Computer Science10, e1905 (2024).https://doi.org/ 10.7717/peerj-cs.1905,https://doi.org/10.7717/peerj-cs.1905.

Diffusion Models Memorize in Training -- and Generalize in Inference PeerJ Computer Science10, e1905 (2024).https://doi.org/ 10.7717/peerj-cs.1905,https://doi.org/10.7717/peerj-cs.1905

Reference 63

Resolution: verified exact; doi, observed 2026-05-21T10:54:07.762369Z

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Observation e6198d68-04d1-4be1-b5ef-25f680ad98ae · outbound

This paper cites an unresolved cited work.

Diffusion Models Memorize in Training -- and Generalize in Inference Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-05-21T10:54:08.269498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e1144afe-6e69-4b66-b356-e702b0c111a3 · outbound

This paper cites an unresolved cited work.

Diffusion Models Memorize in Training -- and Generalize in Inference Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-05-21T10:54:08.283655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 88433fe5-d954-4e40-8aa7-81c55265ead3 · outbound

This paper cites an unresolved cited work.

Diffusion Models Memorize in Training -- and Generalize in Inference Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-05-21T10:54:08.278304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4189ac88-2aee-4699-ba8f-1ee17f67aac3 · outbound

This paper cites Train" shows the average results between 20 different subsets of the training data.

Diffusion Models Memorize in Training -- and Generalize in Inference Train" shows the average results between 20 different subsets of the training data

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T10:54:08.286727Z

Source-reported events for the cited work

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