Paper Citation Record · LEDGER

Noisy Label Refinement with Semantically Reliable Synthetic Images

As of 21 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2509.04298.

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

pith.paper-citation-record.v1
2509.04298 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:17:02.560022Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:17:00.347016Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T10:17:02.643735Z

Reference resolution

25 of 25 outbound references displayed

verified exact1
verified fuzzy15
unresolved7
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9b54581c-a8dc-4113-bbd4-9caa51d607b1 · outbound

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

Noisy Label Refinement with Semantically Reliable Synthetic Images Noisy Label Refinement with Semantically Reliable Synthetic Images

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T10:17:02.647292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9d787659-acac-49ad-b047-f19e7594e3d3 · outbound

This paper cites Noisy label learning Conventional research on noisy label learning [6, 7] was pri- marily based on an i.i.d.

Noisy Label Refinement with Semantically Reliable Synthetic Images Noisy label learning Conventional research on noisy label learning [6, 7] was pri- marily based on an i.i.d

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.824385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5ffa6916-bc1a-437c-b616-670b8bb1120d · outbound

This paper cites A photo of c.

Noisy Label Refinement with Semantically Reliable Synthetic Images A photo of c

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.815236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 25f87d1d-f7b9-4f62-811c-bfd34066c10a · outbound

This paper cites Experimental setup Datasets and noise types.

Noisy Label Refinement with Semantically Reliable Synthetic Images Experimental setup Datasets and noise types

Reference 4

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Observation 51f42e7b-d8ba-4e53-8247-255016aa8452 · outbound

This paper cites an unresolved cited work.

Noisy Label Refinement with Semantically Reliable Synthetic Images Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:17:02.796704Z

Source-reported events for the cited work

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Observation 9022bb07-9034-4aea-9a09-40187a008aaf · outbound

This paper cites Learning with feature- dependent label noise: A progressive approach,.

Noisy Label Refinement with Semantically Reliable Synthetic Images Learning with feature- dependent label noise: A progressive approach,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.787669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7627d908-e725-410f-8e4b-9a110a7b57e8 · outbound

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

Noisy Label Refinement with Semantically Reliable Synthetic Images Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T10:17:00.939302Z

Source-reported events for the cited work

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Observation b1c73cda-f4ee-4976-a753-78dbd25b8a1c · outbound

This paper cites High-resolution im- age synthesis with latent diffusion models,.

Noisy Label Refinement with Semantically Reliable Synthetic Images High-resolution im- age synthesis with latent diffusion models,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.777878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7e1a3cc4-5649-4430-8cb6-aa8a897467f3 · outbound

This paper cites Will large-scale generative models corrupt future datasets?,.

Noisy Label Refinement with Semantically Reliable Synthetic Images Will large-scale generative models corrupt future datasets?,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.767261Z

Source-reported events for the cited work

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Observation a6af25ee-081d-44f5-8ccb-f8948845b2df · outbound

This paper cites Fake it till you make it: Learn- ing transferable representations from synthetic imagenet clones,.

Noisy Label Refinement with Semantically Reliable Synthetic Images Fake it till you make it: Learn- ing transferable representations from synthetic imagenet clones,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.757599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9bcae6a1-5ad8-491e-83a3-92d89ce9890f · outbound

This paper cites Joint optimization framework for learning with noisy labels,.

Noisy Label Refinement with Semantically Reliable Synthetic Images Joint optimization framework for learning with noisy labels,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.747624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 28ce3a3a-2f87-4fce-b67a-31d8058a7e55 · outbound

This paper cites Noisy Annotation Refinement for Object Detection.

Noisy Label Refinement with Semantically Reliable Synthetic Images Noisy Annotation Refinement for Object Detection

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-05T10:17:02.623103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fae547af-968e-4714-9edc-f639b03b6173 · outbound

This paper cites Label- retrieval-augmented diffusion models for learning from noisy labels,.

Noisy Label Refinement with Semantically Reliable Synthetic Images Label- retrieval-augmented diffusion models for learning from noisy labels,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.738006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

This paper cites Is synthetic data from generative models ready for image recognition?.

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

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Observation b8bc3ef9-0b4d-4db1-b1e2-e97cb735cb36 · outbound

This paper cites Imagenet large scale visual recognition challenge,.

Noisy Label Refinement with Semantically Reliable Synthetic Images Imagenet large scale visual recognition challenge,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.727648Z

Source-reported events for the cited work

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Observation f5586c66-70b0-4fbf-9fc6-1140e5cc7389 · outbound

This paper cites Learning multiple layers of features from tiny images,.

Noisy Label Refinement with Semantically Reliable Synthetic Images Learning multiple layers of features from tiny images,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T10:17:02.093559Z

Source-reported events for the cited work

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Observation a7be80ab-17c1-47b9-89ea-0b6d4db31a86 · outbound

This paper cites Adversarial diffusion distillation,.

Noisy Label Refinement with Semantically Reliable Synthetic Images Adversarial diffusion distillation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.711265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5225f9e3-83bb-4716-b866-175aa88ebb97 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Noisy Label Refinement with Semantically Reliable Synthetic Images SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T10:17:02.189047Z

Source-reported events for the cited work

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Observation c3b01973-4702-4293-b88e-008b94073b98 · outbound

This paper cites Deep residual learning for image recognition,.

Noisy Label Refinement with Semantically Reliable Synthetic Images Deep residual learning for image recognition,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T10:17:02.357268Z

Source-reported events for the cited work

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Observation 06476c85-8c2e-457d-b104-a2b33ef62ac4 · outbound

This paper cites Generalized cross en- tropy loss for training deep neural networks with noisy labels,.

Noisy Label Refinement with Semantically Reliable Synthetic Images Generalized cross en- tropy loss for training deep neural networks with noisy labels,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.694873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d1d2709a-ee86-4f01-958b-adc61f9740ac · outbound

This paper cites Symmetric cross entropy for robust learning with noisy labels,.

Noisy Label Refinement with Semantically Reliable Synthetic Images Symmetric cross entropy for robust learning with noisy labels,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.684960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 43b783b0-60d6-475b-907e-43b44215d051 · outbound

This paper cites Error-bounded correction of noisy labels,.

Noisy Label Refinement with Semantically Reliable Synthetic Images Error-bounded correction of noisy labels,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.675818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d5eeee2f-6f05-4421-b793-d528df4cd74d · outbound

This paper cites Centrality and consistency: two-stage clean samples identification for learning with instance- dependent noisy labels,.

Noisy Label Refinement with Semantically Reliable Synthetic Images Centrality and consistency: two-stage clean samples identification for learning with instance- dependent noisy labels,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.666647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 289080ec-a5d5-4019-94cc-ab82c419626b · outbound

This paper cites Learning transferable visual models from natural lan- guage supervision,.

Noisy Label Refinement with Semantically Reliable Synthetic Images Learning transferable visual models from natural lan- guage supervision,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:17:02.657115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation deffef04-547d-41ad-bfa3-8afc8fab1168 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Noisy Label Refinement with Semantically Reliable Synthetic Images An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T10:17:02.560022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 9b54581c-a8dc-4113-bbd4-9caa51d607b1 · inbound