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Paper Citation Record · LEDGER

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models

As of 8 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2509.00378.

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

pith.paper-citation-record.v1
2509.00378 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:44:05.665777Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

24 of 24 outbound references displayed

  • verified exact1
  • verified fuzzy18
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ea7d818e-076f-40b8-975b-4c5e50a51508 · outbound

This paper cites Advances in diffusion models for image data augmentation: A review of methods, models, evaluation metrics and future research directions.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Advances in diffusion models for image data augmentation: A review of methods, models, evaluation metrics and future research directions

Reference 1

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation bf39ae4f-7f82-4af7-8c8e-368117a5f3ee · outbound

This paper cites an unresolved cited work.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Unresolved cited work

Reference 2

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unresolved
raw_fallback, observed 2026-08-05T13:44:10.289410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation cc0f5663-eb7e-4ffa-bab9-7e51735f29d7 · outbound

This paper cites An image is worth one word: Personalizing text-to-image gen- eration using textual inversion.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models An image is worth one word: Personalizing text-to-image gen- eration using textual inversion

Reference 3

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 87efefa9-4e39-4d7d-9663-5a08ab5b2719 · outbound

This paper cites Deep residual learning for image recognition.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Deep residual learning for image recognition

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:09.902208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 1b750dc5-feb5-47e7-8ed4-d14131c757f9 · outbound

This paper cites Classifier-free diffusion guidance.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Classifier-free diffusion guidance

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:09.677992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 39b04404-9499-4c04-9ac3-1d2853f8bc57 · outbound

This paper cites Denoising dif- fusion probabilistic models.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Denoising dif- fusion probabilistic models

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:09.434026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 7e81bb6a-088e-4b60-9657-768c61c32627 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 7

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unresolved
no resolver link, observed 2026-08-05T13:44:03.401762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2c494a1a-422c-4a75-8702-d16728e9837f · outbound

This paper cites Diffusemix: Label- preserving data augmentation with diffusion models.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Diffusemix: Label- preserving data augmentation with diffusion models

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:09.166754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 74d9f377-471f-40d3-b813-8edec15061cc · outbound

This paper cites Genmix: effective data augmentation with generative diffusion model image editing.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Genmix: effective data augmentation with generative diffusion model image editing

Reference 10

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unresolved
no resolver link, observed 2026-08-05T13:44:03.801579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:44:03.801579Z digest=sha256:8ab1181ba62a3ea4d247e669e56e96c9fce913bf97dd5a4fc6c989f52c9e1f15

Observation b52581f3-eb9d-4d3c-abdd-acaa02362f8d · outbound

This paper cites A comprehensive survey of recent trends in deep learn- ing for digital images augmentation.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models A comprehensive survey of recent trends in deep learn- ing for digital images augmentation

Reference 11

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:08.916004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation dfa49df3-3b68-4791-9f2d-847bbc141736 · outbound

This paper cites Adam: A method for stochastic optimization.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Adam: A method for stochastic optimization

Reference 12

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:08.727245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 319c25a2-c7f9-4d20-877b-82e01fad23a5 · outbound

This paper cites Dpm-solver: A fast ode solver for dif- fusion probabilistic model sampling in around 10 steps.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Dpm-solver: A fast ode solver for dif- fusion probabilistic model sampling in around 10 steps

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:08.501375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T13:44:04.193363Z digest=sha256:76b8fce4932720f1ab499e6daf8f0813f18dde4a4f2a5a24b12a68fbb2e53c6a

Observation c53015b6-b4b1-4f0b-97d2-b69fa9d827e6 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Fine-Grained Visual Classification of Aircraft

Reference 14

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unresolved
no resolver link, observed 2026-08-05T13:44:04.288446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3d22edf5-1fd0-4fe0-b2b5-59d52052a12a · outbound

This paper cites Automated flower classification over a large number of classes.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Automated flower classification over a large number of classes

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:08.229240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T13:44:04.417962Z digest=sha256:0555ed44278fc8965a567cae6166eae7073c91c83399c63f5a1a2f312aa6107f

Observation f6bce525-d8c3-452a-a05b-d07b47e5692e · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models High-resolution image syn- thesis with latent diffusion models

Reference 16

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:08.026552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 8c9d571a-6a3d-466b-9e9b-8c6ad58bc6a4 · outbound

This paper cites A survey on image data augmentation for deep learning.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models A survey on image data augmentation for deep learning

Reference 17

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:07.860945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 00de8729-5f8d-412e-8c33-df46a45761fe · outbound

This paper cites Effective data augmentation with diffu- sion models.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Effective data augmentation with diffu- sion models

Reference 18

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:07.600930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 06315654-4c4b-4bd8-9372-f4671d8af89a · outbound

This paper cites The caltech-ucsd birds-200 dataset.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models The caltech-ucsd birds-200 dataset

Reference 19

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:07.325367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation bd38219c-f564-46b3-a329-6a8546e3277f · outbound

This paper cites Enhance im- age classification via inter-class image mixup with diffusion model.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Enhance im- age classification via inter-class image mixup with diffusion model

Reference 20

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:07.055357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 841c0e85-ca2a-446f-a36d-0bebb99f3f3e · outbound

This paper cites Image Data Augmentation for Deep Learning: A Survey.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Image Data Augmentation for Deep Learning: A Survey

Reference 21

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unresolved
no resolver link, observed 2026-08-05T13:44:05.156495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:44:05.156495Z digest=sha256:6f58252fe85cf5df46256c4ec67aaa6a7505c401708d2e78ae2fbc07cbca0b95

Observation 8a009d80-307b-461a-9f10-b7dd2c3a5854 · outbound

This paper cites Real-fake: Effective training data synthesis through distribution matching.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Real-fake: Effective training data synthesis through distribution matching

Reference 22

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:06.912667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 30427d03-54ee-4c89-aa6d-fd6d61b132dd · outbound

This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 23

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:06.741762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T13:44:05.430428Z digest=sha256:b91bd95af243f776e0be5bb5b53347899e20f7e7d53e2eb8018ab0b59aa781fd

Observation 8e1107dd-c153-4bcf-80f5-0a0c4ffac947 · outbound

This paper cites mixup: Beyond empirical risk minimiza- tion.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models mixup: Beyond empirical risk minimiza- tion

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-05T13:44:06.498644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T13:44:05.563564Z digest=sha256:8a8b4b568bd6b56e5f63535c89ef573e5d545ffed9f8d3ba4da2b57dbd81d64b

Observation 2b8f66be-2975-4537-9a49-eb57e77747b9 · outbound

This paper cites A Survey on Data Augmentation in Large Model Era.

NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models A Survey on Data Augmentation in Large Model Era

Reference 25

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verified exact
local_arxiv, observed 2026-08-05T13:44:05.995282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T13:44:05.665777Z digest=sha256:dd450f148dd7a8275836ded11bc50ec1b8346974f87745f34d0e708fe185f042

Pith citing papers

No inbound Pith citation observations are available.