Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction | Paper Citation Record

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

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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

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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

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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

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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

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This paper cites Are we done with ImageNet?.

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

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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

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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

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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

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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

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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

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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

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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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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

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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

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This paper cites Generative adversarial nets.

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

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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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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

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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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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

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This paper cites Denoising dif- fusion probabilistic models.

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

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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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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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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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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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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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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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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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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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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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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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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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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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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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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

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

Reference 40

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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

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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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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

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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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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

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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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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

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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

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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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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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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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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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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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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

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:26.129979Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

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

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:26.107987Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:26.096967Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:25.852618Z

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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

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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

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

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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

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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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 2026-08-18T06:34:40.430872+00:00.

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: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:26.041360Z

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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

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

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