Paper Citation Record · LEDGER

Data Pruning in Generative Diffusion Models

As of 15 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2411.12523.

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

pith.paper-citation-record.v1
2411.12523 v3

Coverage vector

measured 65 of 65 reference resolution

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measured 66 of 66 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: arxiv_reference, observed 2026-05-10T08:37:53.889324Z

Reference resolution

65 of 65 outbound references displayed

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External citation measurements

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Outbound references

Observation f1edcb5f-5508-4f13-a65b-4e1a58e6fe09 · outbound

This paper cites Effective pruning of web-scale datasets based on complexity of concept clusters.

Data Pruning in Generative Diffusion Models Effective pruning of web-scale datasets based on complexity of concept clusters

Reference 1

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Observation 706747ab-9f5b-4473-b90c-f4721a157c51 · outbound

This paper cites Dall- eval: Probing the reasoning skills and social biases of text-to-image generation models.

Data Pruning in Generative Diffusion Models Dall- eval: Probing the reasoning skills and social biases of text-to-image generation models

Reference 2

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Observation 5a82698f-7abf-4ba9-b832-1f23e07d8f5a · outbound

This paper cites Selection via Proxy: Efficient Data Selection for Deep Learning.

Data Pruning in Generative Diffusion Models Selection via Proxy: Efficient Data Selection for Deep Learning

Reference 3

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Observation b8605dc5-f5c7-4e4d-9c82-4b91ba34e334 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Data Pruning in Generative Diffusion Models Diffusion models beat gans on image synthesis

Reference 4

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Observation 4f34ce16-dc39-478b-a80f-21bc154ee1c5 · outbound

This paper cites Ethi- cal considerations and policy interventions concern- ing the impact of generative ai tools in the economy and in society.

Data Pruning in Generative Diffusion Models Ethi- cal considerations and policy interventions concern- ing the impact of generative ai tools in the economy and in society

Reference 5

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Observation b5d80656-af61-4539-b740-44f2905ddebd · outbound

This paper cites What neural networks memorize and why: Discovering the long tail via influence estimation.

Data Pruning in Generative Diffusion Models What neural networks memorize and why: Discovering the long tail via influence estimation

Reference 6

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Observation eb61f89c-0f1d-4994-b6d6-0d68155b4811 · outbound

This paper cites Sparsegpt: Mas- sive language models can be accurately pruned in one-shot.

Data Pruning in Generative Diffusion Models Sparsegpt: Mas- sive language models can be accurately pruned in one-shot

Reference 7

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Observation b82118b2-7808-40a9-a5b1-bc100fa803b4 · outbound

This paper cites The Vendi Score: A Diversity Evaluation Metric for Machine Learning.

Data Pruning in Generative Diffusion Models The Vendi Score: A Diversity Evaluation Metric for Machine Learning

Reference 8

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Observation 54cd503f-9411-4692-90ca-e53eb39feaf1 · outbound

This paper cites Generative adversar- ial nets.

Data Pruning in Generative Diffusion Models Generative adversar- ial nets

Reference 9

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Observation ee80b8c1-c307-4657-9c98-687387d782a4 · outbound

This paper cites Data and parameter scaling laws for neural machine translation.

Data Pruning in Generative Diffusion Models Data and parameter scaling laws for neural machine translation

Reference 10

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Observation 62cb2e88-45f9-44d9-ae92-d8b93da86e19 · outbound

This paper cites Im- proved training of wasserstein gans.

Data Pruning in Generative Diffusion Models Im- proved training of wasserstein gans

Reference 11

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Observation 00ca2062-cf26-478e-8d8a-2bcbaf535b7a · outbound

This paper cites Safety and Fairness for Content Moderation in Generative Models.

Data Pruning in Generative Diffusion Models Safety and Fairness for Content Moderation in Generative Models

Reference 12

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Observation 348f8bb3-4b21-48b9-9fd3-9dd511733512 · outbound

This paper cites Smaller coresets for k-median and k-means clustering.

Data Pruning in Generative Diffusion Models Smaller coresets for k-median and k-means clustering

Reference 13

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Observation 829efd60-95a3-4c4e-b3a7-a4648cbde12f · outbound

This paper cites Large-scale dataset pruning with dynamic un- certainty.

Data Pruning in Generative Diffusion Models Large-scale dataset pruning with dynamic un- certainty

Reference 14

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Observation c869c407-b12a-4de7-a2a0-9bb6c0a6be87 · outbound

This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

Data Pruning in Generative Diffusion Models Imagen Video: High Definition Video Generation with Diffusion Models

Reference 15

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Observation e545fd51-19b7-44fd-9ea4-42fa547d74c5 · outbound

This paper cites Denois- ing diffusion probabilistic models.

Data Pruning in Generative Diffusion Models Denois- ing diffusion probabilistic models

Reference 16

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Observation 1620967c-09da-41a0-b344-83c135b367a6 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Data Pruning in Generative Diffusion Models Training Compute-Optimal Large Language Models

Reference 17

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Observation f064620a-fe59-4339-acaa-ae9fd5d1b0c7 · outbound

This paper cites Data distribution search to select core-set for machine learning.

Data Pruning in Generative Diffusion Models Data distribution search to select core-set for machine learning

Reference 18

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Observation 1945f1a6-08c0-40e8-9d08-d4ee00c30fa5 · outbound

This paper cites Scaling Laws for Neural Language Models.

Data Pruning in Generative Diffusion Models Scaling Laws for Neural Language Models

Reference 19

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Observation 1d8c9931-7ea3-41ed-9be0-9e30872e3905 · outbound

This paper cites PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection.

Data Pruning in Generative Diffusion Models PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection

Reference 20

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Observation 42ff20d7-3b9c-47d3-ae22-4a4f5c60a608 · outbound

This paper cites Denoising diffusion restoration mod- 13 els.

Data Pruning in Generative Diffusion Models Denoising diffusion restoration mod- 13 els

Reference 21

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Observation df471151-2abc-486c-8d79-b5910530ea7b · outbound

This paper cites Grad-match: Gradient matching based data subset selection for efficient deep model training.

Data Pruning in Generative Diffusion Models Grad-match: Gradient matching based data subset selection for efficient deep model training

Reference 22

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Observation 49064f17-4c05-430b-9dea-d47e4bc0bcfb · outbound

This paper cites Harmful biases in artificial intelli- gence.

Data Pruning in Generative Diffusion Models Harmful biases in artificial intelli- gence

Reference 23

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Observation 2c493c8c-3d5e-4578-b8cd-28a3fb60a85f · outbound

This paper cites Auto-Encoding Variational Bayes.

Data Pruning in Generative Diffusion Models Auto-Encoding Variational Bayes

Reference 24

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This paper cites VideoPoet: A Large Language Model for Zero-Shot Video Generation.

Data Pruning in Generative Diffusion Models VideoPoet: A Large Language Model for Zero-Shot Video Generation

Reference 25

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This paper cites Improved precision and recall metric for assessing generative models.Ad- vances in neural information processing systems , 32,.

Data Pruning in Generative Diffusion Models Improved precision and recall metric for assessing generative models.Ad- vances in neural information processing systems , 32,

Reference 26

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Observation 528c687c-8f99-4dbc-a15b-1b5990a9ca42 · outbound

This paper cites Holistic evaluation of text-to-image models.

Data Pruning in Generative Diffusion Models Holistic evaluation of text-to-image models

Reference 27

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Observation 1b81c39b-c540-4eeb-bcf9-dd9f4a9ef0da · outbound

This paper cites Diffusion models for image restoration and enhancement–a comprehensive survey.

Data Pruning in Generative Diffusion Models Diffusion models for image restoration and enhancement–a comprehensive survey

Reference 28

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Observation 9d275999-016c-41b6-987f-717b61e1d381 · outbound

This paper cites Flow Matching for Generative Modeling.

Data Pruning in Generative Diffusion Models Flow Matching for Generative Modeling

Reference 29

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This paper cites SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers.

Data Pruning in Generative Diffusion Models SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 30

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Observation 147ff7e2-7fd1-465c-9580-fb6c0a07a615 · outbound

This paper cites Analyzing Quality, Bias, and Performance in Text-to-Image Generative Models.

Data Pruning in Generative Diffusion Models Analyzing Quality, Bias, and Performance in Text-to-Image Generative Models

Reference 31

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This paper cites A non-parametric test to detect data- copying in generative models.

Data Pruning in Generative Diffusion Models A non-parametric test to detect data- copying in generative models

Reference 32

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Observation 77c38a07-6ceb-4846-8d03-1901cc60a8dc · outbound

This paper cites Rdcgan: Un- supervised representation learning with regularized deep convolutional generative adversarial networks.

Data Pruning in Generative Diffusion Models Rdcgan: Un- supervised representation learning with regularized deep convolutional generative adversarial networks

Reference 33

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Observation dbfa4190-dd5d-4ce2-b3d7-4d0e769fe5c6 · outbound

This paper cites Coresets for data-efficient training of ma- chine learning models.

Data Pruning in Generative Diffusion Models Coresets for data-efficient training of ma- chine learning models

Reference 34

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This paper cites Geometry- complete diffusion for 3d molecule generation and optimization.

Data Pruning in Generative Diffusion Models Geometry- complete diffusion for 3d molecule generation and optimization

Reference 35

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Observation 19e811bd-49af-4722-8ada-17ae09302d11 · outbound

This paper cites Diffusion models, image super- resolution, and everything: A survey.

Data Pruning in Generative Diffusion Models Diffusion models, image super- resolution, and everything: A survey

Reference 36

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Data Pruning in Generative Diffusion Models Unresolved cited work

Reference 37

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This paper cites Deep learning on a data diet: Finding im- portant examples early in training.

Data Pruning in Generative Diffusion Models Deep learning on a data diet: Finding im- portant examples early in training

Reference 38

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This paper cites Scalable Diffusion Models with Transformers.

Data Pruning in Generative Diffusion Models Scalable Diffusion Models with Transformers

Reference 39

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This paper cites Learning transferable visual models 14 from natural language supervision.

Data Pruning in Generative Diffusion Models Learning transferable visual models 14 from natural language supervision

Reference 40

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Data Pruning in Generative Diffusion Models Neural synthesis of binaural speech from mono audio

Reference 41

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Observation e74fadd4-5e91-4d49-b06a-e7a5b35f4bf5 · outbound

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Data Pruning in Generative Diffusion Models High- resolution image synthesis with latent diffusion mod- els, 2021

Reference 42

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Observation 92bbfd92-20c8-4b01-8186-c245c7b8c3d4 · outbound

This paper cites Assessing gen- erative models via precision and recall.

Data Pruning in Generative Diffusion Models Assessing gen- erative models via precision and recall

Reference 43

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Observation 6d9f93b3-8059-4263-909e-d1bfdeedb72a · outbound

This paper cites What Matters In The Structured Pruning of Generative Language Models?.

Data Pruning in Generative Diffusion Models What Matters In The Structured Pruning of Generative Language Models?

Reference 44

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This paper cites Generative model- ing by estimating gradients of the data distribution.

Data Pruning in Generative Diffusion Models Generative model- ing by estimating gradients of the data distribution

Reference 45

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Observation 74edff51-6ea7-4da7-bff5-f4a23b2ba5e0 · outbound

This paper cites Solving Inverse Problems in Medical Imaging with Score-Based Generative Models.

Data Pruning in Generative Diffusion Models Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

Reference 46

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This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Data Pruning in Generative Diffusion Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 47

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This paper cites Beyond neural scal- ing laws: beating power law scaling via data pruning.

Data Pruning in Generative Diffusion Models Beyond neural scal- ing laws: beating power law scaling via data pruning

Reference 48

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This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Data Pruning in Generative Diffusion Models A Simple and Effective Pruning Approach for Large Language Models

Reference 49

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Observation 553a3d8d-0ad5-4540-a032-f4e793600d76 · outbound

This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.

Data Pruning in Generative Diffusion Models Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 50

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This paper cites Data pruning via moving-one-sample-out.

Data Pruning in Generative Diffusion Models Data pruning via moving-one-sample-out

Reference 51

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Observation fa26aa31-6ff6-41d8-8b69-aa8cd3c49523 · outbound

This paper cites Struc- tured pruning for efficient generative pre-trained lan- guage models.

Data Pruning in Generative Diffusion Models Struc- tured pruning for efficient generative pre-trained lan- guage models

Reference 52

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Observation a1b84676-57ba-43db-9d02-6dbc6e58340b · outbound

This paper cites On measuring fairness in gener- ative models.

Data Pruning in Generative Diffusion Models On measuring fairness in gener- ative models

Reference 53

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Observation 6bbf40b4-ba0a-4e9d-a979-2d6aae1a2027 · outbound

This paper cites Fair generative models via trans- fer learning.

Data Pruning in Generative Diffusion Models Fair generative models via trans- fer learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:30:10.154862Z

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Observation cee222b3-624c-4200-9cfd-9c0ebb902cea · outbound

This paper cites An Empirical Study of Example Forgetting during Deep Neural Network Learning.

Data Pruning in Generative Diffusion Models An Empirical Study of Example Forgetting during Deep Neural Network Learning

Reference 55

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Observation 7020b87b-55e5-448b-95ff-e9f717825ec2 · outbound

This paper cites Neural discrete representation learning.

Data Pruning in Generative Diffusion Models Neural discrete representation learning

Reference 56

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Observation f7ef9d55-2e58-466c-8f76-19ac1ad2c8a2 · outbound

This paper cites Diffusion models for medical image reconstruction.

Data Pruning in Generative Diffusion Models Diffusion models for medical image reconstruction

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:30:10.111771Z

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Observation d89fe1a7-9b6d-40e1-975e-0dcdbdebdad0 · outbound

This paper cites Moderate coreset: A universal method of data selection for real-world data-efficient deep learning.

Data Pruning in Generative Diffusion Models Moderate coreset: A universal method of data selection for real-world data-efficient deep learning

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:30:10.085763Z

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Observation a7ffc02c-45a8-4989-bf3a-1272a3c66e98 · outbound

This paper cites GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation.

Data Pruning in Generative Diffusion Models GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation

Reference 59

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Observation 0978cd7c-9510-4b85-81ae-579a07f6a89a · outbound

This paper cites Mind the boundary: Coreset selection via reconstructing the decision boundary.

Data Pruning in Generative Diffusion Models Mind the boundary: Coreset selection via reconstructing the decision boundary

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:30:10.065133Z

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Observation 09f878c8-c5c8-4970-a9f7-58512c2447f5 · outbound

This paper cites Dataset Pruning: Reducing Training Data by Examining Generalization Influence.

Data Pruning in Generative Diffusion Models Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 61

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Observation 6779157c-a8d4-42a7-9597-b107f0d0e392 · outbound

This paper cites A generalized dual-domain gen- erative framework with hierarchical consistency for 15 medical image reconstruction and synthesis.

Data Pruning in Generative Diffusion Models A generalized dual-domain gen- erative framework with hierarchical consistency for 15 medical image reconstruction and synthesis

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:30:10.040956Z

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Observation 511bbc1b-0173-4c88-851a-bf5ed1e63277 · outbound

This paper cites The unreasonable ef- fectiveness of deep features as a perceptual metric.

Data Pruning in Generative Diffusion Models The unreasonable ef- fectiveness of deep features as a perceptual metric

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:30:10.014124Z

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Observation 1f3bb838-4a15-4e8f-bb78-d02fc884e4a4 · outbound

This paper cites Energy-efficient high-fidelity image reconstruction with memristor arrays for medical di- agnosis.

Data Pruning in Generative Diffusion Models Energy-efficient high-fidelity image reconstruction with memristor arrays for medical di- agnosis

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:30:09.993906Z

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Observation 7d5cd2d2-1bc0-428b-bab4-29c560d59292 · outbound

This paper cites Coverage-centric Coreset Selection for High Pruning Rates.

Data Pruning in Generative Diffusion Models Coverage-centric Coreset Selection for High Pruning Rates

Reference 65

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Pith citing papers

Observation 59dfb9e8-0460-4583-8369-135db21b8058 · inbound

The Amazing Stability of Flow Matching cites this paper.

The Amazing Stability of Flow Matching Data Pruning in Generative Diffusion Models

Reference 5

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