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Paper Citation Record · LEDGER

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction

As of 23 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2508.19581.

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pith.paper-citation-record.v1
2508.19581 v1

Coverage vector

measured 65 of 65 reference resolution

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measured 65 of 65 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

65 of 65 outbound references displayed

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External citation measurements

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Outbound references

Observation 7932e332-4f35-4a30-bea9-e7b167231423 · outbound

This paper cites Reverse-time diffusion equation mod- els.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Reverse-time diffusion equation mod- els

Reference 1

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Observation 31256320-3e8e-49ea-bee0-97ea075cf206 · outbound

This paper cites From noisy predic- tion to true label: Noisy prediction calibration via generative model.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction From noisy predic- tion to true label: Noisy prediction calibration via generative model

Reference 2

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Observation 48af4903-274e-456d-b687-127892d12a80 · outbound

This paper cites Understand- ing and improving early stopping for learning with noisy la- bels.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Understand- ing and improving early stopping for learning with noisy la- bels

Reference 3

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Observation f9c7cb0f-95c0-4a19-95c9-3bcafbebeb39 · outbound

This paper cites Frozen in time: A joint video and image encoder for end-to-end retrieval.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Frozen in time: A joint video and image encoder for end-to-end retrieval

Reference 4

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Observation f6684826-91bd-4c17-a4b2-2114bdfd2726 · outbound

This paper cites A Note on the Inception Score.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction A Note on the Inception Score

Reference 5

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Observation ccfdb4d1-f2b2-4285-8155-5d02e1634476 · outbound

This paper cites Are we done with ImageNet?.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Are we done with ImageNet?

Reference 6

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Observation ecfb8d59-adcb-4cb1-a294-5cab63d6954c · outbound

This paper cites Food-101–mining discriminative components with random forests.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Food-101–mining discriminative components with random forests

Reference 7

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Observation b02c03eb-c91a-416b-8a11-6f22ceb616f8 · outbound

This paper cites Denoising likelihood score match- ing for conditional score-based data generation.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Denoising likelihood score match- ing for conditional score-based data generation

Reference 8

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Observation 7af612d7-21e2-48cc-804c-ffe91ba98481 · outbound

This paper cites Slight corruption in pre-training data makes better dif- fusion models.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Slight corruption in pre-training data makes better dif- fusion models

Reference 9

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Observation 9e83f5f1-8248-48f3-88b2-c43300408b57 · outbound

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

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Label-retrieval- augmented diffusion models for learning from noisy labels

Reference 10

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Observation c238f85b-855b-4872-8924-380fcda9e24c · outbound

This paper cites Learning with instance-dependent label noise: A sample sieve approach, 2021.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Learning with instance-dependent label noise: A sample sieve approach, 2021

Reference 11

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Observation 2cdd10d8-b597-4eb1-b3ef-829df7c52d4b · outbound

This paper cites Mitigating Memorization of Noisy Labels via Regularization between Representations.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Mitigating Memorization of Noisy Labels via Regularization between Representations

Reference 12

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Observation f36ba01a-c46c-4ae7-9e5b-4abfb41a842e · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Imagenet: A large-scale hierarchical image database

Reference 13

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Observation c4882d3f-6f36-4b7b-8caa-df12a68fb1be · outbound

This paper cites Diffusion models beat gans on image synthesis.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Diffusion models beat gans on image synthesis

Reference 14

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Observation dcccb6b7-8418-490a-b44e-223209b7e6a0 · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 15

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Observation eaf431c7-570b-42c5-88a0-8ee4a6aee97b · outbound

This paper cites Generative adversarial nets.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Generative adversarial nets

Reference 16

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Observation 785459ef-6237-4945-98bb-d982f0a6061d · outbound

This paper cites Noise-contrastive estimation: A new estimation principle for unnormalized statistical models.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

Reference 17

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Observation 6d5b9213-c997-4e63-906c-d923bfbd3efa · outbound

This paper cites Co- teaching: Robust training of deep neural networks with ex- tremely noisy labels.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Co- teaching: Robust training of deep neural networks with ex- tremely noisy labels

Reference 18

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Observation 3b9ea062-63dc-4b97-a2ae-285601494d3b · outbound

This paper cites Improving generalization by controlling label- noise information in neural network weights.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Improving generalization by controlling label- noise information in neural network weights

Reference 19

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Observation 544cab6b-3839-4d31-a620-76803ed491ce · outbound

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

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 20

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Observation 58bdf165-6d94-4e71-96bb-e4258a5dec22 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Denoising dif- fusion probabilistic models

Reference 21

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Observation 1b468f0c-bc0f-4b57-a11f-91e74d69daee · outbound

This paper cites Label-noise robust generative adversarial networks.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Label-noise robust generative adversarial networks

Reference 22

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Observation 5a7c8b4c-494a-4f73-9ba6-fcd81864c1e9 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Elucidating the design space of diffusion-based generative models

Reference 23

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Observation 61c5c67a-b308-453c-9096-cd360ab14c1a · outbound

This paper cites Refining generative process with discriminator guidance in score-based diffusion mod- els.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Refining generative process with discriminator guidance in score-based diffusion mod- els

Reference 24

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Observation 681cb76b-b6ff-48a7-8dc4-16a965e421c8 · outbound

This paper cites Learning discriminative dynamics with label corruption for noisy label detection.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Learning discriminative dynamics with label corruption for noisy label detection

Reference 25

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Observation c497e7d2-d595-4377-807d-f63a6e17fb60 · outbound

This paper cites Nlnl: Negative learning for noisy labels.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Nlnl: Negative learning for noisy labels

Reference 26

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Observation f3992461-a878-422e-974d-c1041b803325 · outbound

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

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Learning multiple layers of features from tiny images

Reference 27

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Observation bdc59720-3813-4d16-9a9a-ca53c01143ac · outbound

This paper cites Learning with noisy labels by efficient transition ma- trix estimation to combat label miscorrection.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Learning with noisy labels by efficient transition ma- trix estimation to combat label miscorrection

Reference 28

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Observation b9cda7d3-9f11-4a86-8502-d14a5693b91a · outbound

This paper cites Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models

Reference 29

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Observation 7e5b4892-fe93-45d8-a134-1ad8f7206808 · outbound

This paper cites Tiny imagenet visual recognition challenge.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Tiny imagenet visual recognition challenge

Reference 30

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Observation fad7d6f7-68f3-486f-9448-b4165485c297 · outbound

This paper cites Robust Training with Ensemble Consensus.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Robust Training with Ensemble Consensus

Reference 31

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Observation fbfbed6d-7977-43d8-bf5c-82f649fea38e · outbound

This paper cites Neighbor- hood collective estimation for noisy label identification and correction.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Neighbor- hood collective estimation for noisy label identification and correction

Reference 32

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Observation fd44de83-e542-4812-8f81-b4510602e2be · outbound

This paper cites Early-learning regularization pre- vents memorization of noisy labels.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Early-learning regularization pre- vents memorization of noisy labels

Reference 33

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Observation 45220469-45ba-4f49-9977-38d4fdc0d469 · outbound

This paper cites Label- noise robust diffusion models.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Label- noise robust diffusion models

Reference 34

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Observation 6b2ae70f-d331-419c-a555-547ed5b7c50b · outbound

This paper cites Reliable fidelity and diversity metrics for generative models.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Reliable fidelity and diversity metrics for generative models

Reference 35

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Observation f98c976e-6ef4-49c6-9a73-c7b2dcd389fb · outbound

This paper cites Confident learning: Estimating uncertainty in dataset labels.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Confident learning: Estimating uncertainty in dataset labels

Reference 36

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Observation c96f2b20-500a-43c9-8e5e-0b27cebc98d9 · outbound

This paper cites Memorization in deep neural net- works: Does the loss function matter? In Pacific-Asia Con- ference on Knowledge Discovery and Data Mining , pages 131–142.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Memorization in deep neural net- works: Does the loss function matter? In Pacific-Asia Con- ference on Knowledge Discovery and Data Mining , pages 131–142

Reference 37

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Observation c1617b0c-e854-4e2a-8f3b-a3e1478b196a · outbound

This paper cites Identifying mislabeled data using the area under the margin ranking.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Identifying mislabeled data using the area under the margin ranking

Reference 38

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

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Observation f9b9eef9-3777-4dda-8615-e0b48fbe6aad · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Learning transferable visual models from natural language supervi- sion

Reference 39

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Observation dca8fccb-76e9-4e4e-88b2-1e165235cd2a · outbound

This paper cites Classification accuracy score for conditional generative models.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Classification accuracy score for conditional generative models

Reference 40

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

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Observation 2ebfdcf8-76e8-4920-83ec-d9ec737ff87e · outbound

This paper cites Improved techniques for training gans.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Improved techniques for training gans

Reference 41

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

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Observation 4775c156-26bd-4381-934f-63a2269e2947 · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 42

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Observation 6e5d085c-a2af-425b-a189-04cf2e56fae4 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Deep unsupervised learning using nonequilibrium thermodynamics

Reference 43

Resolution
verified fuzzy
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Source-reported events for the cited work

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

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Observation 40a6b3b6-b3d8-4238-abac-72e0ae54656a · outbound

This paper cites Denoising Diffusion Implicit Models.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Denoising Diffusion Implicit Models

Reference 44

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Source-reported events for the cited work

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Observation 67190ed6-e0c9-41b0-a0c0-87678e6dd665 · outbound

This paper cites Generative modeling by esti- mating gradients of the data distribution.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Generative modeling by esti- mating gradients of the data distribution

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:57:26.199410Z

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.

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Observation 7e34c857-2c7a-4fe3-b0f0-9733de078bed · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Score-Based Generative Modeling through Stochastic Differential Equations

Reference 46

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Observation c92a7dc9-af03-46ac-a6cd-e4490c016ed4 · outbound

This paper cites Robustness of conditional gans to noisy la- bels.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Robustness of conditional gans to noisy la- bels

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:57:26.188457Z

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Observation d156c88f-781d-4df1-b561-d23f2fe5aac0 · outbound

This paper cites Identifying and eliminating csam in genera- tive ml training data and models.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Identifying and eliminating csam in genera- tive ml training data and models

Reference 48

Resolution
verified fuzzy
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Source-reported events for the cited work

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

source=pdf_text observed=2026-08-15T16:57:25.811219Z digest=sha256:7374a9df98419866f0a508ab91bcef93148ccd16ef2d3720bd4bbdf67bebd7e0

Observation c3c991aa-3d85-4ac0-8696-06002b672a65 · outbound

This paper cites A connection between score matching and denoising autoencoders.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction A connection between score matching and denoising autoencoders

Reference 49

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Observation 5d85a29a-5fa7-40a5-a6cc-f1ad550b8538 · outbound

This paper cites DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models

Reference 50

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Unavailable: canonical work link unavailable.

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Observation 48b60186-7a76-46e0-8617-9dfb7547a379 · outbound

This paper cites Robust early-learning: Hindering the memorization of noisy labels.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Robust early-learning: Hindering the memorization of noisy labels

Reference 51

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verified fuzzy
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Source-reported events for the cited work

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Observation 07011f42-689a-4684-91fd-c5846c2d4a6c · outbound

This paper cites Part-dependent label noise: Towards instance-dependent label noise.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Part-dependent label noise: Towards instance-dependent label noise

Reference 52

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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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T16:57:25.826191Z digest=sha256:dbfa4c1fcfb282f85fcd11b405a3e34f34c3acb35f313c55a9ee3f0366271d74

Observation 44350125-8e48-4541-b866-ac1784026cb9 · outbound

This paper cites Sample Selection with Uncertainty of Losses for Learning with Noisy Labels.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Sample Selection with Uncertainty of Losses for Learning with Noisy Labels

Reference 53

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Observation d0d44ec4-f084-485b-83db-1ffc12cf3c4c · outbound

This paper cites Learning from massive noisy labeled data for im- age classification.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Learning from massive noisy labeled data for im- age classification

Reference 54

Resolution
unresolved
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Unavailable: canonical work link unavailable.

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Observation f58580ac-cd95-4743-85e1-49ea3685e133 · outbound

This paper cites Dual t: Reduc- ing estimation error for transition matrix in label-noise learn- ing.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Dual t: Reduc- ing estimation error for transition matrix in label-noise learn- ing

Reference 55

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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 2026-08-23T06:30:58.430688+00:00.

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Observation 8c51f9d8-3477-459c-b059-2ca138b76213 · outbound

This paper cites On learning contrastive representations for learning with noisy labels.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction On learning contrastive representations for learning with noisy labels

Reference 56

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verified fuzzy
raw_fallback, observed 2026-08-15T16:57:26.118641Z

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.

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Observation 2e406368-30f0-42c9-b87c-f219702c2d34 · outbound

This paper cites How does disagreement help gener- alization against label corruption? In International confer- ence on machine learning, pages 7164–7173.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction How does disagreement help gener- alization against label corruption? In International confer- ence on machine learning, pages 7164–7173

Reference 57

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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.

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Observation ce519c61-3c09-4e8c-a454-e95367872269 · outbound

This paper cites Understanding deep learning (still) requires rethinking generalization.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Understanding deep learning (still) requires rethinking generalization

Reference 58

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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.

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Observation d8cd3c7b-3447-445c-bb50-bccad8c7fcb0 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction mixup: Beyond Empirical Risk Minimization

Reference 59

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Unavailable: canonical work link unavailable.

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Observation b7a1fc30-76b5-4cdd-8464-3fa026cfd9e6 · outbound

This paper cites Learning noise transition matrix from only noisy labels via total varia- tion regularization.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Learning noise transition matrix from only noisy labels via total varia- tion regularization

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:57:26.086054Z

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.

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Observation 15a94b8b-2e30-49ae-b622-55715868e371 · outbound

This paper cites Differentiable augmentation for data-efficient gan training.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Differentiable augmentation for data-efficient gan training

Reference 61

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verified fuzzy
raw_fallback, observed 2026-08-15T16:57:26.075051Z

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.

source=pdf_text observed=2026-08-15T16:57:25.859642Z digest=sha256:0d1df5c0bd5e56e4edfd8118b38fd0a623f95302683551c7af8328f61ebca3e5

Observation 3e68d148-5cfd-4e0d-94e5-e43a9c3c7eda · outbound

This paper cites Robust cur- riculum learning: from clean label detection to noisy label self-correction.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Robust cur- riculum learning: from clean label detection to noisy label self-correction

Reference 62

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verified fuzzy
raw_fallback, observed 2026-08-15T16:57:26.064223Z

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.

source=pdf_text observed=2026-08-15T16:57:25.863043Z digest=sha256:4be159b45803ac5963716208ba33934ab010d795203b22ab54dde172e4750d55

Observation f1f820e7-16e5-4c4e-9948-d943979e269f · outbound

This paper cites Detecting cor- rupted labels without training a model to predict.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction Detecting cor- rupted labels without training a model to predict

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:57:26.053336Z

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.

source=pdf_text observed=2026-08-15T16:57:25.866799Z digest=sha256:a7b07b3348c32d7ad0a14e545404b19a8ec27171ac5a5d561def5495ad5f539f

Observation 4259e448-ec91-4a16-91b0-18d70f6e18c5 · outbound

This paper cites First, given the objective in Eq.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction First, given the objective in Eq

Reference 64

Resolution
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raw_fallback, observed 2026-08-15T16:57:26.041360Z

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.

source=pdf_text observed=2026-08-15T16:57:25.870361Z digest=sha256:0cfed56054c73304aea9d318f3072cc8ee512c948d1cf566af58d238ec032adb

Observation eeca84cd-a7b0-40ad-a2c0-bb17e6d0a0fe · outbound

This paper cites unconditional term.

Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction unconditional term

Reference 65

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malformed identifier
raw_fallback, observed 2026-08-15T16:57:26.028339Z

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.

source=pdf_text observed=2026-08-15T16:57:25.874756Z digest=sha256:d588eb84a875e7b98a5598785ef1816414cd2dd0178a07f1139c0809244aaa75

Pith citing papers

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