Paper Citation Record · LEDGER

Is synthetic data from generative models ready for image recognition?

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 29 inbound Pith citation observations for arXiv:2210.07574.

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

pith.paper-citation-record.v1
2210.07574 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 29 of 29 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:36:22.992440Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T09:45:40.415009Z

Reference resolution

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External citation measurements

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Outbound references

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Pith citing papers

Observation d6bf533c-c497-4f96-b3e3-ed8ccc311bda · inbound

Improvement in Facial Emotion Recognition using Synthetic Data Generated by Diffusion Model cites this paper.

Improvement in Facial Emotion Recognition using Synthetic Data Generated by Diffusion Model Is synthetic data from generative models ready for image recognition?

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T19:18:15.990889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b42cfc1-8891-4a8e-b26d-d56663279635 · inbound

Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough? cites this paper.

Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough? Is synthetic data from generative models ready for image recognition?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T10:49:00.522078Z

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Observation 167ca046-86cd-40d5-b63f-46896d7ddf23 · inbound

Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data cites this paper.

Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data Is synthetic data from generative models ready for image recognition?

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T19:57:41.037858Z

Source-reported events for the cited work

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Observation 2da199c9-c244-41e3-a70c-97c72ab12a8b · inbound

OccScene: Semantic Occupancy-based Cross-task Mutual Learning for 3D Scene Generation cites this paper.

OccScene: Semantic Occupancy-based Cross-task Mutual Learning for 3D Scene Generation Is synthetic data from generative models ready for image recognition?

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T15:17:38.681766Z

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Observation fb51f3d9-a5ec-4569-b5a1-12f098664c81 · inbound

Dataset Augmentation by Mixing Visual Concepts cites this paper.

Dataset Augmentation by Mixing Visual Concepts Is synthetic data from generative models ready for image recognition?

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T11:32:38.988962Z

Source-reported events for the cited work

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Observation 91776a6a-e230-48a4-9f27-d838befc6bab · inbound

Autonomous Crack Detection using Deep Learning on Synthetic Thermogram Datasets cites this paper.

Autonomous Crack Detection using Deep Learning on Synthetic Thermogram Datasets Is synthetic data from generative models ready for image recognition?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T10:34:42.911407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8eb57996-6dd5-4afa-9c53-86d97271b1a9 · inbound

Discriminative Image Generation with Diffusion Models for Zero-Shot Learning cites this paper.

Discriminative Image Generation with Diffusion Models for Zero-Shot Learning Is synthetic data from generative models ready for image recognition?

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T05:47:18.871965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c589d14-8a62-474b-8b3f-54bdd15c57b9 · inbound

An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval cites this paper.

An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval Is synthetic data from generative models ready for image recognition?

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-22T23:02:13.648759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 43f2225f-9d78-4822-8e0d-489090480a42 · inbound

Unsupervised Learning for Class Distribution Mismatch cites this paper.

Unsupervised Learning for Class Distribution Mismatch Is synthetic data from generative models ready for image recognition?

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T22:36:22.992440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30c65c69-1c03-4064-8359-904db1834762 · inbound

Does Feasibility Matter? Understanding the Impact of Feasibility on Synthetic Training Data cites this paper.

Does Feasibility Matter? Understanding the Impact of Feasibility on Synthetic Training Data Is synthetic data from generative models ready for image recognition?

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T21:10:42.107261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01a133c2-618d-45e6-b2dd-eac8978168ef · inbound

EarthSynth: Generating Informative Earth Observation with Diffusion Models cites this paper.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Is synthetic data from generative models ready for image recognition?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T20:45:56.548166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67b64720-6b74-4e83-ae1f-fbab2eda55dd · inbound

Generative Data Augmentation for Object Point Cloud Segmentation cites this paper.

Generative Data Augmentation for Object Point Cloud Segmentation Is synthetic data from generative models ready for image recognition?

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:44:43.815200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f12becd5-2a12-452d-b2f9-b7b5de9ce869 · inbound

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation cites this paper.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Is synthetic data from generative models ready for image recognition?

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:16.931612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20a68a1d-e3a2-4eb3-a58f-49d96ec5a5d5 · inbound

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation cites this paper.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Is synthetic data from generative models ready for image recognition?

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:15.570823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5251441-0a8b-4390-8f88-04f93ba1fe46 · inbound

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery cites this paper.

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Is synthetic data from generative models ready for image recognition?

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T20:04:01.714423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b573f621-05f3-47c2-ace2-655eb5a8c9d2 · inbound

Edge-case Synthesis for Fisheye Object Detection: A Data-centric Perspective cites this paper.

Edge-case Synthesis for Fisheye Object Detection: A Data-centric Perspective Is synthetic data from generative models ready for image recognition?

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T15:18:06.780393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc4a2ee5-c593-4188-a4b6-cdca5ded8ab0 · inbound

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting cites this paper.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Is synthetic data from generative models ready for image recognition?

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:17.644406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0efc5cc-9b65-4bbd-8064-e55be8f5400e · inbound

DoorDet: Semi-Automated Multi-Class Door Detection Dataset via Object Detection and Large Language Models cites this paper.

DoorDet: Semi-Automated Multi-Class Door Detection Dataset via Object Detection and Large Language Models Is synthetic data from generative models ready for image recognition?

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T22:02:24.041143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cd1c357-0bce-4d37-9d5b-5c1fad07ec33 · inbound

Enhancing Zero-Shot Pedestrian Attribute Recognition with Synthetic Data Generation: A Comparative Study with Image-To-Image Diffusion Models cites this paper.

Enhancing Zero-Shot Pedestrian Attribute Recognition with Synthetic Data Generation: A Comparative Study with Image-To-Image Diffusion Models Is synthetic data from generative models ready for image recognition?

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T11:53:05.422168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e5eea64-60b3-417f-91e7-11f4f39f776b · inbound

Noisy Label Refinement with Semantically Reliable Synthetic Images cites this paper.

Noisy Label Refinement with Semantically Reliable Synthetic Images Is synthetic data from generative models ready for image recognition?

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T10:17:01.818069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d30919a-469e-4a3e-9163-c976570d76b7 · inbound

Exploring Cross-Modal Flows for Few-Shot Learning cites this paper.

Exploring Cross-Modal Flows for Few-Shot Learning Is synthetic data from generative models ready for image recognition?

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-18T06:20:58.945504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5e5fefb6-9f4e-49e7-a280-8db299e7e632 · inbound

Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method cites this paper.

Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method Is synthetic data from generative models ready for image recognition?

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T08:07:37.618570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa159330-4121-4a8a-8e79-959a5537da5b · inbound

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? cites this paper.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? Is synthetic data from generative models ready for image recognition?

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:44.477234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7feb0d5-3409-482a-808b-e8e85549bf40 · inbound

All in One: A Unified Synthetic Data Pipeline for Multimodal Video Understanding cites this paper.

All in One: A Unified Synthetic Data Pipeline for Multimodal Video Understanding Is synthetic data from generative models ready for image recognition?

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:31:03.831703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0ff88d79-2643-4dd0-83c7-e703cb9e722b · inbound

Mutual Enhancement Between Global Tokens and Patch Tokens: From Theory to Practice cites this paper.

Mutual Enhancement Between Global Tokens and Patch Tokens: From Theory to Practice Is synthetic data from generative models ready for image recognition?

Reference 80

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T22:43:50.948798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8af1a31f-3564-4a74-98a1-a0e3174442c2 · inbound

What Makes Synthetic Data Effective in Image Segmentation cites this paper.

What Makes Synthetic Data Effective in Image Segmentation Is synthetic data from generative models ready for image recognition?

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-20T07:13:06.678571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 11c6bb25-f64f-4dfd-b6cb-ad4cc834769d · inbound

Personalized Generative Models for Contextual Debiasing cites this paper.

Personalized Generative Models for Contextual Debiasing Is synthetic data from generative models ready for image recognition?

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:13:59.771709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5bfc6e4b-2ed7-4ebc-8438-4e5367190f0b · inbound

AC3S: Adaptive Conditioning for 3D-Aware Synthetic Data Generation cites this paper.

AC3S: Adaptive Conditioning for 3D-Aware Synthetic Data Generation Is synthetic data from generative models ready for image recognition?

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-01T09:45:40.416507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation eab662ee-0bb0-4ad6-a007-255629963a0f · inbound

Free-Lunch Augmentation by Revisiting Diffusion-Based Data Generation for Cross-Domain Few-Shot Object Detection cites this paper.

Free-Lunch Augmentation by Revisiting Diffusion-Based Data Generation for Cross-Domain Few-Shot Object Detection Is synthetic data from generative models ready for image recognition?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:50.888768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.