Paper Citation Record · LEDGER

An overview of diffusion models for generative artificial intelligence

As of 12 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2412.01371.

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

pith.paper-citation-record.v1
2412.01371 v1

Coverage vector

measured 54 of 54 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

54 of 54 outbound references displayed

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External citation measurements

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Outbound references

Observation a1bdd74c-fd80-4df5-9951-9951c072b29c · outbound

This paper cites GPT-4 Technical Report.

An overview of diffusion models for generative artificial intelligence GPT-4 Technical Report

Reference 1

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This paper cites Comparison of image quality assessment: Psnr, hvs, ssim, uiqi.

An overview of diffusion models for generative artificial intelligence Comparison of image quality assessment: Psnr, hvs, ssim, uiqi

Reference 2

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This paper cites Structured Denoising Diffusion Models in Discrete State-Spaces.

An overview of diffusion models for generative artificial intelligence Structured Denoising Diffusion Models in Discrete State-Spaces

Reference 3

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This paper cites Learning theory from first principles.

An overview of diffusion models for generative artificial intelligence Learning theory from first principles

Reference 4

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This paper cites Bayesian reasoning and machine learning.

An overview of diffusion models for generative artificial intelligence Bayesian reasoning and machine learning

Reference 5

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This paper cites Improving image generation with better captions.

An overview of diffusion models for generative artificial intelligence Improving image generation with better captions

Reference 6

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An overview of diffusion models for generative artificial intelligence Unresolved cited work

Reference 7

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This paper cites Diffusion Models Beat GANs on Image Synthesis.

An overview of diffusion models for generative artificial intelligence Diffusion Models Beat GANs on Image Synthesis

Reference 8

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This paper cites Derivations for linear algebra and optimization.

An overview of diffusion models for generative artificial intelligence Derivations for linear algebra and optimization

Reference 9

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This paper cites Deep Learning.

An overview of diffusion models for generative artificial intelligence Deep Learning

Reference 11

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Observation 371fc63c-7a9b-4f2f-8586-47896033091b · outbound

This paper cites Generative adversarial nets.

An overview of diffusion models for generative artificial intelligence Generative adversarial nets

Reference 12

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An overview of diffusion models for generative artificial intelligence Unresolved cited work

Reference 13

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Observation 1bce4e38-d9d9-4d63-a514-01f684966777 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

An overview of diffusion models for generative artificial intelligence Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 14

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Observation 710eb56d-e0c3-4863-96d9-3e51e139ec09 · outbound

This paper cites Denoising diffusion probabilistic models.

An overview of diffusion models for generative artificial intelligence Denoising diffusion probabilistic models

Reference 15

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Observation ab2758ef-472e-4d87-9801-f694b25e9b85 · outbound

This paper cites Classifier-Free Diffusion Guidance.

An overview of diffusion models for generative artificial intelligence Classifier-Free Diffusion Guidance

Reference 16

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This paper cites Video Diffusion Models.

An overview of diffusion models for generative artificial intelligence Video Diffusion Models

Reference 17

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Observation c3a69699-314e-4519-ae80-ab7506c958c5 · outbound

This paper cites Image quality metrics: Psnr vs.

An overview of diffusion models for generative artificial intelligence Image quality metrics: Psnr vs

Reference 18

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Observation 30443328-0964-4b8b-ac3e-dadebb730afd · outbound

This paper cites Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory.

An overview of diffusion models for generative artificial intelligence Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 19

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Observation e832f98a-c2ff-4c9b-9f37-9118d1c940f7 · outbound

This paper cites Analyzing and improving the image quality of stylegan.

An overview of diffusion models for generative artificial intelligence Analyzing and improving the image quality of stylegan

Reference 20

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Observation b7f6c566-ebbb-4a11-bcbf-bcb159126899 · outbound

This paper cites DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation.

An overview of diffusion models for generative artificial intelligence DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation

Reference 21

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This paper cites P., and Welling, M.

An overview of diffusion models for generative artificial intelligence P., and Welling, M

Reference 22

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This paper cites Probability theory: a comprehensive course.

An overview of diffusion models for generative artificial intelligence Probability theory: a comprehensive course

Reference 23

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An overview of diffusion models for generative artificial intelligence Unresolved cited work

Reference 24

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This paper cites A tutorial on energy- based learning.

An overview of diffusion models for generative artificial intelligence A tutorial on energy- based learning

Reference 25

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Observation e49a555b-c09e-4881-87dc-cf7863956d4c · outbound

This paper cites Diffusion-LM Improves Controllable Text Generation.

An overview of diffusion models for generative artificial intelligence Diffusion-LM Improves Controllable Text Generation

Reference 26

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This paper cites Improved Techniques for Training Score-Based Generative Models.

An overview of diffusion models for generative artificial intelligence Improved Techniques for Training Score-Based Generative Models

Reference 27

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An overview of diffusion models for generative artificial intelligence Microsoft COCO: Common Objects in Context

Reference 28

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This paper cites Fully convolutional networks for semantic segmen- tation.

An overview of diffusion models for generative artificial intelligence Fully convolutional networks for semantic segmen- tation

Reference 29

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This paper cites Improved Denoising Diffusion Probabilistic Models.

An overview of diffusion models for generative artificial intelligence Improved Denoising Diffusion Probabilistic Models

Reference 30

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An overview of diffusion models for generative artificial intelligence Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors

Reference 31

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An overview of diffusion models for generative artificial intelligence Understanding SSIM

Reference 32

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This paper cites Learning Transferable Visual Models From Natural Language Supervision.

An overview of diffusion models for generative artificial intelligence Learning Transferable Visual Models From Natural Language Supervision

Reference 33

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An overview of diffusion models for generative artificial intelligence Unresolved cited work

Reference 34

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

An overview of diffusion models for generative artificial intelligence Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 35

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An overview of diffusion models for generative artificial intelligence Zero-shot text-to-image generation

Reference 36

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An overview of diffusion models for generative artificial intelligence Variational Inference with Normalizing Flows

Reference 37

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An overview of diffusion models for generative artificial intelligence High-resolution image synthesis with latent diffusion models

Reference 38

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An overview of diffusion models for generative artificial intelligence An overview of gradient descent optimization algorithms

Reference 39

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An overview of diffusion models for generative artificial intelligence Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Reference 40

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An overview of diffusion models for generative artificial intelligence J., and Norouzi, M

Reference 41

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An overview of diffusion models for generative artificial intelligence Improved Techniques for Training GANs

Reference 42

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An overview of diffusion models for generative artificial intelligence Understanding machine learning: From theory to algorithms

Reference 43

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An overview of diffusion models for generative artificial intelligence Deep unsupervised learning using nonequilibrium thermodynamics

Reference 44

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An overview of diffusion models for generative artificial intelligence Denoising Diffusion Implicit Models

Reference 45

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An overview of diffusion models for generative artificial intelligence Rethinking the inception architecture for computer vision

Reference 46

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An overview of diffusion models for generative artificial intelligence Conditional Image Generation with PixelCNN Decoders

Reference 47

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An overview of diffusion models for generative artificial intelligence N., Kaiser, L

Reference 48

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An overview of diffusion models for generative artificial intelligence Image quality assessment: from error visibility to structural similarity

Reference 49

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Observation 50f55dff-baf1-48b9-8f7e-63eec9e508d9 · outbound

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An overview of diffusion models for generative artificial intelligence Diffusion Models for Medical Anomaly Detection

Reference 50

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An overview of diffusion models for generative artificial intelligence Group Normalization

Reference 51

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An overview of diffusion models for generative artificial intelligence M., and Willcocks, C

Reference 52

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Observation 1a1bfcf6-3d12-480b-9bbe-3ebb9686f527 · outbound

This paper cites Diffusion Probabilistic Modeling for Video Generation.

An overview of diffusion models for generative artificial intelligence Diffusion Probabilistic Modeling for Video Generation

Reference 53

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An overview of diffusion models for generative artificial intelligence A., Shechtman, E., and W ang, O

Reference 54

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Observation d9a096a8-4ce4-4036-b24d-9306bf4a1922 · outbound

This paper cites Towards language-free training for text-to-image generation.

An overview of diffusion models for generative artificial intelligence Towards language-free training for text-to-image generation

Reference 55

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Pith citing papers

No inbound Pith citation observations are available.