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Paper Citation Record · LEDGER

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach

As of 13 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2501.00954.

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

pith.paper-citation-record.v1
2501.00954 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:41:06.173249Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

23 of 23 outbound references displayed

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External citation measurements

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Outbound references

Observation 9db33269-a64a-4b26-937c-7f0aa25469e2 · outbound

This paper cites Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images

Reference 1

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doi, observed 2026-08-10T22:41:06.470164Z

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Observation 5e781f35-f31b-4d14-a924-457d35446eba · outbound

This paper cites The Deep Learning Computer Model in Reading Diabetic Retinopathy & Normal Images.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach The Deep Learning Computer Model in Reading Diabetic Retinopathy & Normal Images

Reference 2

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 4ca1cc85-dcef-4add-be10-d2874f8da0a8 · outbound

This paper cites Alias-Free Generative Adversarial Networks.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach Alias-Free Generative Adversarial Networks

Reference 3

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Observation 06739004-aa8d-4546-b7e9-7c025528dba0 · outbound

This paper cites Augmenting medical image classifiers with synthetic data from latent diffusion models.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach Augmenting medical image classifiers with synthetic data from latent diffusion models

Reference 4

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Observation b0ee16db-4032-48ea-9381-a25544c80307 · outbound

This paper cites RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network

Reference 5

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Observation bc122ad9-7439-431c-b2a0-331d31ad215e · outbound

This paper cites Generative Adversarial Networks.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach Generative Adversarial Networks

Reference 6

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Observation f4d369fb-1520-423f-b640-09a9367c1ac7 · outbound

This paper cites A Style-Based Generator Architecture for Generative Adversarial Networks.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach A Style-Based Generator Architecture for Generative Adversarial Networks

Reference 7

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Observation a98752ba-3960-414d-be23-bad0ff93eb7d · outbound

This paper cites Analyzing and Improving the Image Quality of StyleGAN.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach Analyzing and Improving the Image Quality of StyleGAN

Reference 8

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Observation 322dea1b-b11c-42fe-8692-e1b44543622f · outbound

This paper cites Learning Generalized Transformation Equivariant Representations via Autoencoding Transformations.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach Learning Generalized Transformation Equivariant Representations via Autoencoding Transformations

Reference 9

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local_arxiv, observed 2026-08-10T22:41:06.382535Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 6ff82ab2-5ef7-4f97-9062-05bb544c6a0e · outbound

This paper cites Image Quality Metrics: PSNR vs.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach Image Quality Metrics: PSNR vs

Reference 10

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source=pdf_text observed=2026-08-10T22:41:06.118570Z digest=sha256:ac049aaff463576042bc7622f55bbd74976199542ece2450bbb404f1e94a1984

Observation 720485c9-4a28-47f8-9a8c-b39ca2044eac · outbound

This paper cites Which Training Methods for GANs do actually Converge?.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach Which Training Methods for GANs do actually Converge?

Reference 11

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Observation 372d9323-f266-484c-b723-70667b49955c · outbound

This paper cites MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks

Reference 12

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Observation 57a422c5-0990-40c4-9f4a-d270d7b0be9f · outbound

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

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 13

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Observation 6f960fc3-7e92-4334-a823-379c2ad9ac21 · outbound

This paper cites Demystifying MMD GANs.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach Demystifying MMD GANs

Reference 14

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Observation 5b40cd57-3eb8-4a66-a2d0-acf5ae71bd7a · outbound

This paper cites The Lie Derivative for Measuring Learned Equivariance.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach The Lie Derivative for Measuring Learned Equivariance

Reference 15

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local_arxiv, observed 2026-08-10T22:41:06.295510Z

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Observation 4f03ddb0-3a82-4e19-b070-0ae617ff60f1 · outbound

This paper cites Computing Machinery and Intelligence.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach Computing Machinery and Intelligence

Reference 16

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Observation c443838e-5618-4da9-84dc-f4fcc8b5fbda · outbound

This paper cites Bootstrap Resampling.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach Bootstrap Resampling

Reference 17

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doi, observed 2026-08-10T22:41:06.267202Z

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Observation 81e472d4-9081-4399-9790-98aee43eab87 · outbound

This paper cites An Analysis of Variance Test for Normality (Complete Samples).

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach An Analysis of Variance Test for Normality (Complete Samples)

Reference 18

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Observation 7f922d93-2fe6-4d35-a93b-489bcb2916e8 · outbound

This paper cites The Mann-Whitney U: A Test for Assessing Whether Two Independent Samples Come from the Same Distribution.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach The Mann-Whitney U: A Test for Assessing Whether Two Independent Samples Come from the Same Distribution

Reference 19

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Observation da5c8f1f-e131-4694-8137-34bc066b5f50 · outbound

This paper cites Blockwise Spectral Analysis for Deepfake Detection in High-Fidelity Videos.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach Blockwise Spectral Analysis for Deepfake Detection in High-Fidelity Videos

Reference 20

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source=pdf_text observed=2026-08-10T22:41:06.160575Z digest=sha256:f6e60714233cf02918d16bbc4b80d02faa8446617cb866ad2d43a965f9ef8016

Observation 806a4602-c9d8-4b73-8a61-84b3e3f59701 · outbound

This paper cites Exploring the Asynchronous of the Frequency Spectra of GAN-generated Facial Images.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach Exploring the Asynchronous of the Frequency Spectra of GAN-generated Facial Images

Reference 21

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local_arxiv, observed 2026-08-10T22:41:06.232379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 913ccb82-472d-4eea-9942-3a5f50ffa8a4 · outbound

This paper cites The Fast Fourier Transform.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach The Fast Fourier Transform

Reference 22

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Observation ce8055c8-eba2-4b12-be8a-1ae805a5c9e2 · outbound

This paper cites Power spectrum in the cave.

Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach Power spectrum in the cave

Reference 23

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Pith citing papers

No inbound Pith citation observations are available.