COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement | Paper Citation Record

Observation 0a1789b6-5762-498c-af7c-a3f30b6390d5 · outbound

This paper cites Image classification with deep learning in the presence of noisy labels: A survey.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Image classification with deep learning in the presence of noisy labels: A survey

Reference 1

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Observation 231a4eed-9df2-4273-9a59-12d27bcf02fa · outbound

This paper cites Wills aligner: Multi-subject collaborative brain visual decoding.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Wills aligner: Multi-subject collaborative brain visual decoding

Reference 2

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Observation 68917ffa-c106-4407-b067-0fd647a7b0e1 · outbound

This paper cites Evaluating Machine Unlearning via Epistemic Uncertainty.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Evaluating Machine Unlearning via Epistemic Uncertainty

Reference 3

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Observation f053c072-cb71-4f62-acd4-b5f681e2e90e · outbound

This paper cites On mixup regularization.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement On mixup regularization

Reference 4

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Observation 80c75f33-720b-4cad-955b-c3b9ff43cd56 · outbound

This paper cites Machine unlearning via null space calibration.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Machine unlearning via null space calibration

Reference 5

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Observation abecd5b9-a3dd-4d4b-bf05-2c4e651ba719 · outbound

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

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Learning with instance-dependent label noise: A sample sieve approach

Reference 6

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Observation 5a6d83bc-ed60-46dc-b12a-63d378207f58 · outbound

This paper cites Label smoothing improves machine unlearning, 2024.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Label smoothing improves machine unlearning, 2024

Reference 7

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Observation ed6cd7d7-5570-4311-aad5-66ed0dcbf205 · outbound

This paper cites Learning, unlearning, and relearning: Using web 2.0 technologies to support the development of lifelong learning skills.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Learning, unlearning, and relearning: Using web 2.0 technologies to support the development of lifelong learning skills

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:42.250705Z

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Observation 02f6abf4-4f8a-4c54-90f3-96d5cee7d2d0 · outbound

This paper cites Generalized jensen-shannon divergence loss for learning with noisy labels.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Generalized jensen-shannon divergence loss for learning with noisy labels

Reference 9

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Observation 6c781f56-077b-46ff-a2e1-f524d23c9406 · outbound

This paper cites Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation

Reference 10

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Observation 465b5445-954f-4356-ad6d-ef5ad7754c50 · outbound

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COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Unresolved cited work

Reference 11

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Observation 20a55bbd-6d6e-413e-bd62-6ef117e0cdb6 · outbound

This paper cites Eternal sunshine of the spotless net: Selective forgetting in deep networks.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Eternal sunshine of the spotless net: Selective forgetting in deep networks

Reference 12

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Observation 3ec59e50-c560-479b-b5fb-1e2fa3509cc3 · outbound

This paper cites Neuroclips: Towards high-fidelity and smooth fmri-to-video reconstruction.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Neuroclips: Towards high-fidelity and smooth fmri-to-video reconstruction

Reference 13

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This paper cites Amnesiac machine learning.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Amnesiac machine learning

Reference 14

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Observation b6cdbb6f-1247-4808-9db5-bf9854f2cae7 · outbound

This paper cites Tsang, and Masashi Sugiyama.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Tsang, and Masashi Sugiyama

Reference 15

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Observation 1500faee-0cb3-45ac-b0bc-58978650a30f · outbound

This paper cites Approximate data deletion from machine learning models.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Approximate data deletion from machine learning models

Reference 16

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Observation 79f740bd-8464-4ead-b892-887a9dce59b6 · outbound

This paper cites Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels

Reference 17

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Observation 61ed062e-e874-4a1f-b00b-46a3411e195f · outbound

This paper cites UNICON: combating label noise through uniform selection and contrastive learning.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement UNICON: combating label noise through uniform selection and contrastive learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:41.953914Z

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Observation 53b9bf5f-db7e-42e7-b13f-1d37424d0108 · outbound

This paper cites Nlnl: Negative learning for noisy labels.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Nlnl: Negative learning for noisy labels

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:41.868643Z

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Observation fbe41bd1-914e-4f4e-ab12-3778c2f83751 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Overcoming catastrophic forgetting in neural networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:41.772104Z

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Observation 9a74c5bd-0c9d-49d7-89ce-40a04aec9ddb · outbound

This paper cites Learning, unlearning, and relearning: Lessons from one school's approach to creating and sustaining learning communities.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Learning, unlearning, and relearning: Lessons from one school's approach to creating and sustaining learning communities

Reference 21

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Observation d4447381-e407-4b22-a516-e1f9e6b05aa4 · outbound

This paper cites Understanding black-box predictions via influence functions.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Understanding black-box predictions via influence functions

Reference 22

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

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Learning multiple layers of features from tiny images

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:41.743964Z

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COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Unresolved cited work

Reference 24

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This paper cites an unresolved cited work.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Unresolved cited work

Reference 25

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Observation 06c4e66d-e951-4e39-b091-2ac30d2e7819 · outbound

This paper cites Disc: Learning from noisy labels via dynamic instance-specific selection and correction.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Disc: Learning from noisy labels via dynamic instance-specific selection and correction

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:41.544020Z

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Observation 8336ce9d-9616-4c63-8937-c6c1b11b4b09 · outbound

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

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Early-learning regularization prevents memorization of noisy labels

Reference 27

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This paper cites Model sparsity can simplify machine unlearning.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Model sparsity can simplify machine unlearning

Reference 28

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Observation 2756a96f-0b34-4e15-afef-90ab46a27882 · outbound

This paper cites Does label smoothing mitigate label noise? In ICML , pages 6448--6458.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Does label smoothing mitigate label noise? In ICML , pages 6448--6458

Reference 29

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COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement when to update

Reference 30

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Observation 75a46866-f332-4d34-addd-c64806872be4 · outbound

This paper cites Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks

Reference 31

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

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Automated flower classification over a large number of classes

Reference 32

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This paper cites O'Connor, and Kevin McGuinness.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement O'Connor, and Kevin McGuinness

Reference 33

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This paper cites Parkhi, Andrea Vedaldi, Andrew Zisserman, and C.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Parkhi, Andrea Vedaldi, Andrew Zisserman, and C

Reference 34

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This paper cites Learn, unlearn and relearn: An online learning paradigm for deep neural networks.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Learn, unlearn and relearn: An online learning paradigm for deep neural networks

Reference 35

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This paper cites Forgetting as a form of adaptive engram cell plasticity.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Forgetting as a form of adaptive engram cell plasticity

Reference 36

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This paper cites Noisy concurrent training for efficient learning under label noise.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Noisy concurrent training for efficient learning under label noise

Reference 37

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This paper cites "Forgetting" in Machine Learning and Beyond: A Survey.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement "Forgetting" in Machine Learning and Beyond: A Survey

Reference 38

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COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Rethinking the inception architecture for computer vision

Reference 39

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This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 40

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Observation 439acb88-06e6-4ecf-a6d5-52996df1b1b8 · outbound

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

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Symmetric cross entropy for robust learning with noisy labels

Reference 41

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Observation 3b0fbd1d-6c58-45ab-bb44-90c25eed3d5a · outbound

This paper cites Machine Unlearning of Features and Labels.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Machine Unlearning of Features and Labels

Reference 42

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This paper cites Combating noisy labels by agreement: A joint training method with co-regularization.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Combating noisy labels by agreement: A joint training method with co-regularization

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:41.049265Z

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Observation d730baf1-5ce9-404a-b0cc-00df44a52134 · outbound

This paper cites To smooth or not? when label smoothing meets noisy labels.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement To smooth or not? when label smoothing meets noisy labels

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:41.041411Z

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Observation a0bef616-a2dc-4f91-831d-939921eb2d2b · outbound

This paper cites L \_ dmi: A novel information-theoretic loss function for training deep nets robust to label noise.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement L \_ dmi: A novel information-theoretic loss function for training deep nets robust to label noise

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:41.034363Z

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This paper cites an unresolved cited work.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:38:41.027653Z

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Observation fcfdac15-d76d-4ec9-92ec-c88beae234e9 · outbound

This paper cites Probabilistic end-to-end noise correction for learning with noisy labels.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Probabilistic end-to-end noise correction for learning with noisy labels

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:41.021603Z

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Observation 8ff5dce6-42e9-4fa5-94c8-4291351504d1 · outbound

This paper cites Tsang, and Masashi Sugiyama.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Tsang, and Masashi Sugiyama

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:41.013135Z

Source-reported events for the cited work

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Observation e25e5791-e8db-4e33-9da4-be174b40638a · outbound

This paper cites Wide residual networks.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Wide residual networks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:40.943772Z

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COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Unresolved cited work

Reference 50

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This paper cites Dauphin, and David Lopez-Paz.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Dauphin, and David Lopez-Paz

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:40.722032Z

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Observation a13f6626-cfa4-41d0-a88d-11abf92d74c2 · outbound

This paper cites Tripartite collaborative filtering with observability and selection for debiasing rating estimation on missing-not-at-random data.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Tripartite collaborative filtering with observability and selection for debiasing rating estimation on missing-not-at-random data

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:40.702610Z

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

Observation 0786f50c-5aed-415a-9874-7e823896fcd0 · outbound

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

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Learning with feature-dependent label noise: A progressive approach

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:40.694833Z

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Observation edeb1628-5366-42e6-ba89-2f115d21b11c · outbound

This paper cites Learning with noisy labels via sparse regularization.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement Learning with noisy labels via sparse regularization

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:38:40.686513Z

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This paper cites write newline.

COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement write newline

Reference 55

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Pith citing papers