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Paper Citation Record · LEDGER

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning

As of 17 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2502.07721.

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

pith.paper-citation-record.v1
2502.07721 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:50:06.426488Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

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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

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation a9c05f2b-69a6-4154-895c-ed44329ac889 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Imagenet classification with deep convolutional neural networks,

Reference 1

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Observation 43a26c74-8950-4ac0-afca-da2bc2f23b07 · outbound

This paper cites Deep residual learning for image recognition,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Deep residual learning for image recognition,

Reference 2

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Observation c7176360-ca84-43f6-9606-b8c5755ec3d6 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 3

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Observation 6aab5677-5285-4f9c-b5ab-ac920faa6a15 · outbound

This paper cites Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,

Reference 4

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Observation 40dd1717-50ce-4c69-a18c-815ee9843b73 · outbound

This paper cites Classification in the presence of label noise: a survey,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Classification in the presence of label noise: a survey,

Reference 5

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Observation 8b001dc8-5ec3-459b-acf6-207481d05d37 · outbound

This paper cites Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks

Reference 6

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Observation d05f8349-1fc0-49db-a81f-d6b115f5c882 · outbound

This paper cites Un- derstanding deep learning (still) requires rethinking generalization,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Un- derstanding deep learning (still) requires rethinking generalization,

Reference 7

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Observation 4fb74bcb-4c6f-4d36-af8a-96edda39410c · outbound

This paper cites Image classification with noisy labels: a comprehensive survey,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Image classification with noisy labels: a comprehensive survey,

Reference 8

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Observation 64e73b8a-d23e-4cef-ba0e-86611ea1d121 · outbound

This paper cites A closer look at memorization in deep networks,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning A closer look at memorization in deep networks,

Reference 9

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Observation 6767b92d-3c98-422e-ac75-20a9e19dfdc4 · outbound

This paper cites Gender shades: Intersectional accuracy disparities in commercial gender classification,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Gender shades: Intersectional accuracy disparities in commercial gender classification,

Reference 10

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Observation b8dbf84c-1a63-4e90-a2c5-c59f0b137342 · outbound

This paper cites Explaining and harnessing adversarial examples,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Explaining and harnessing adversarial examples,

Reference 11

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Observation b73b9e76-bdca-4af6-b180-3fa893f8446a · outbound

This paper cites Intriguing properties of neural networks,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Intriguing properties of neural networks,

Reference 12

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Observation c1abee93-5af7-4293-a433-a1c5b0724854 · outbound

This paper cites Robust loss functions under label noise for deep neural networks,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Robust loss functions under label noise for deep neural networks,

Reference 13

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Observation e8e685b6-234f-4373-9363-9538d38e27d2 · outbound

This paper cites Using pre- training can improve model robustness and uncertainty,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Using pre- training can improve model robustness and uncertainty,

Reference 14

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Observation 1f172e57-04e8-4770-ae97-2ebb0ee8c855 · outbound

This paper cites Making deep neural networks robust to label noise: A loss correction approach,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Making deep neural networks robust to label noise: A loss correction approach,

Reference 15

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Observation 1aac5e48-7f60-4426-a5bb-a42652224cc8 · outbound

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

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels,

Reference 16

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Observation 972ad0c8-3d8e-4884-804c-a6d30796cd87 · outbound

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

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Co-teaching: Robust training of deep neural networks with extremely noisy labels,

Reference 17

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Observation d1db9936-d1ff-4235-aa37-f9e03926c9ea · outbound

This paper cites Training deep neural networks on noisy labels with bootstrap- ping,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Training deep neural networks on noisy labels with bootstrap- ping,

Reference 18

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Observation d536629d-6ae1-4600-826e-0128eae49eeb · outbound

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

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Joint optimization framework for learning with noisy labels,

Reference 19

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Observation df71f282-cc37-41ea-94bb-1b40162d22ec · outbound

This paper cites Learning to reweight ex- amples for robust deep learning,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Learning to reweight ex- amples for robust deep learning,

Reference 20

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Observation af22039d-d7e7-4bf6-9659-be0e454d5d51 · outbound

This paper cites Learning to learn from noisy labeled data,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Learning to learn from noisy labeled data,

Reference 21

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Observation 048f282b-0742-4057-af5b-e68d82f4246a · outbound

This paper cites How does disagreement help generalization against label corruption?.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning How does disagreement help generalization against label corruption?

Reference 22

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Observation 76e115c7-5112-400e-b032-0dbfe3ef91d4 · outbound

This paper cites Robust estimation of a location parameter,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Robust estimation of a location parameter,

Reference 23

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Observation bd301f78-d962-4ec2-a19f-a6c32a01c6a0 · outbound

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

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Generalized cross entropy loss for training deep neural networks with noisy labels,

Reference 24

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Observation 5288162a-4577-4efb-86fd-96eda8096ea1 · outbound

This paper cites Northcutt, T.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Northcutt, T

Reference 25

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Observation cc38692a-8b31-4ec9-927c-e80e43c12890 · outbound

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

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Early-learning regularization prevents memorization of noisy labels,

Reference 26

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Observation 9ab46fed-9ddc-4912-8111-a3ee9eeb0194 · outbound

This paper cites Unsupervised label noise modeling and loss correction,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Unsupervised label noise modeling and loss correction,

Reference 27

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Observation 7e566e2a-5d9d-4bcf-9272-ef8997355a9c · outbound

This paper cites Temporal Ensembling for Semi-Supervised Learning.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Temporal Ensembling for Semi-Supervised Learning

Reference 28

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Observation 11b35cb9-2d74-4bde-8a55-e0a2e7797005 · outbound

This paper cites Virtual adversarial training: a regularization method for supervised and semi-supervised learning,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Virtual adversarial training: a regularization method for supervised and semi-supervised learning,

Reference 29

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Observation 726eed57-9ef2-404d-8225-5293c1762afb · outbound

This paper cites Iterative learning with open-set noisy labels,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Iterative learning with open-set noisy labels,

Reference 30

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Observation fa05a69b-43d5-4060-ada6-1ab6cd0531a1 · outbound

This paper cites Settles, Active learning literature survey.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Settles, Active learning literature survey

Reference 31

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Observation eabb37cc-b670-44e4-bbde-26853983468f · outbound

This paper cites Dividemix: Learning with noisy labels as semi-supervised learning,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Dividemix: Learning with noisy labels as semi-supervised learning,

Reference 32

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Observation b43befa8-3845-4572-8e96-7cbf360375fe · outbound

This paper cites Probabilistic end-to-end noise correction for learn- ing with noisy labels,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Probabilistic end-to-end noise correction for learn- ing with noisy labels,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation ad819c95-7a10-4c4c-a927-7c8a1c1a6f43 · outbound

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

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Error-bounded correction of noisy labels,

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 645c1dd1-1564-4b5b-bdd8-67d964a4a666 · outbound

This paper cites Topological structural relation learning for zero-shot and few-shot image classification under label noise,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Topological structural relation learning for zero-shot and few-shot image classification under label noise,

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 59c06a21-c766-4218-a826-a231cfee736d · outbound

This paper cites Noise detection and label correction via likelihood estimation for medical image classification,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Noise detection and label correction via likelihood estimation for medical image classification,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T11:50:06.748827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-08T11:50:06.376188Z digest=sha256:ee978c66ec85296b3ba15760bcc73ef9b0ca2ec6c6794af1c0a54dd48bd85a26

Observation 04854676-7c87-4875-a565-2da8c1612dc8 · outbound

This paper cites Classification with noisy labels by importance reweighting,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Classification with noisy labels by importance reweighting,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T11:50:06.736590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-08T11:50:06.380012Z digest=sha256:4b079455201fe937f01d8c10d08f3a703b7928ac522a9d3cfc188eb1c826db11

Observation 1c848404-7ed2-4f22-9089-fdff17b1d7c2 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-08T11:50:06.383841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:50:06.383841Z digest=sha256:f2d875cbb536af2308ece4dea0d5b9313440490399d79a0b98c1a10c051e6cf3

Observation fdcf45b6-3fa2-475c-a2a9-6bc8656b984d · outbound

This paper cites Prototypical networks for few- shot learning,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Prototypical networks for few- shot learning,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T11:50:06.719139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-08T11:50:06.387363Z digest=sha256:47fe450fa4c229772ec18f38a45989264675103e18741e4ed53c823514408800

Observation 59ce9273-2dac-4217-b2c7-3c2400192104 · outbound

This paper cites Learning to learn by gradient descent by gradient descent,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Learning to learn by gradient descent by gradient descent,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T11:50:06.707295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-08T11:50:06.390512Z digest=sha256:4fd95470947399b25db68d96bfb99d4213702aebdd2b6a7e4aaa7e81fca7238c

Observation 1d130361-c433-4b2e-b2a7-50e5c34cf8e6 · outbound

This paper cites Meta networks,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Meta networks,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T11:50:06.695628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-08T11:50:06.393941Z digest=sha256:e3202e58c44b7b2c002501b547d43b664f1991563454e2b58865c673edfe561a

Observation 052f7b61-b11f-47ce-97ba-fe6758f4994b · outbound

This paper cites A survey on transfer learning,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning A survey on transfer learning,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T11:50:06.684180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-08T11:50:06.397215Z digest=sha256:be008c87c1c51450b4aca1c09d6c409a1b07f0a37578fc73e6b444c24c205d89

Observation 6ad25b71-2589-4e39-9c97-a6167bbddc1d · outbound

This paper cites Transfer learning for reinforcement learning domains: A survey,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Transfer learning for reinforcement learning domains: A survey,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T11:50:06.673417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-08T11:50:06.400586Z digest=sha256:3d3802a1d3db7a5ec5b9b87381c545e12c8a57c26b3b244a7b9e7b2c54efa820

Observation 4e7af0a6-8f32-4772-b3ed-4d1d51e1d7a1 · outbound

This paper cites Domain-adversarial training of neural networks,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Domain-adversarial training of neural networks,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T11:50:06.661860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-08T11:50:06.403275Z digest=sha256:694cb08a816c064cdbcc69cbd7c02eb86dec7803ddbcb5a8f598208cea6b8a96

Observation 0d8fe971-c98a-4d88-9f12-f0ddb424089f · outbound

This paper cites Long short-term memory,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Long short-term memory,

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-08T11:50:06.406191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:50:06.406191Z digest=sha256:bf75cf12cb66d08af73a02081c75e8c0ef911158be1fb75760899ea931dba42d

Observation e8ba04f1-d136-4c98-8828-703b95130cdc · outbound

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

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Learning multiple layers of features from tiny images,

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-08T11:50:06.408864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:50:06.408864Z digest=sha256:8adfa8177eac158d021bc54c3023e3863ea63cc8c162d0cfcea2e68a66573231

Observation c2c7ba2f-a0f7-42d5-a021-2304d92cc470 · outbound

This paper cites Learning from massive noisy labeled data for image classification,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Learning from massive noisy labeled data for image classification,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T11:50:06.638490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-08T11:50:06.411730Z digest=sha256:78fd32a8067dae39e462c59ab83cf1f9c232830d5c88ffad96fbd95034dc35a8

Observation d880e4cc-237a-4e77-a532-6ff14fd6245b · outbound

This paper cites WebVision Database: Visual Learning and Understanding from Web Data.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning WebVision Database: Visual Learning and Understanding from Web Data

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-08T11:50:06.414417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:50:06.414417Z digest=sha256:0026972e00bf270fcae1f4cf853353a41fcac5cca413f68d747b4bc6ad4971a1

Observation 1ccae616-b45b-445e-9d87-1e141ac5e6f2 · outbound

This paper cites Decoupling.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Decoupling

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T11:50:06.627170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-08T11:50:06.417998Z digest=sha256:9d7498e00472ceef9f51a270f2764f508d69c28fde68778a7c0453208290eb89

Observation f25c54e2-60ad-461f-a038-208191f679c5 · outbound

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

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Imagenet: A large-scale hierarchical image database,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T11:50:06.517937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-08T11:50:06.420791Z digest=sha256:eff2103e65f9327d87d526222c9616bc6e77521f13c3362c4d5df42e99c4a596

Observation 7dd95853-80b6-48d4-9282-2d7584af1710 · outbound

This paper cites Adam: A method for stochastic optimization,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Adam: A method for stochastic optimization,

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-08T11:50:06.423497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:50:06.423497Z digest=sha256:657277a064221288babe27b8faa268c490fd4bc1057f02db3d5cd5200c14eecc

Observation 97513210-9c80-497b-bf40-d887a3e3ce2a · outbound

This paper cites Visualizing data using t-sne,.

TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning Visualizing data using t-sne,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T11:50:06.499068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-08T11:50:06.426488Z digest=sha256:9331c8b7d71bed30d646d82d765cf54c73d66967bf0e61fa033ec537ce3123fe

Pith citing papers

No inbound Pith citation observations are available.