Paper Citation Record · LEDGER

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition

As of 13 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2411.18533.

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

pith.paper-citation-record.v1
2411.18533 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:08:38.030615Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

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verified fuzzy21
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External citation measurements

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Outbound references

Observation 998799d9-e55c-4f9f-b34e-8fba26fc1bd2 · outbound

This paper cites A review study on ml- based methods for defect-pattern recognition in wafer maps,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition A review study on ml- based methods for defect-pattern recognition in wafer maps,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:39.113959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 40865fed-741f-405f-bfff-4b9ce3dff6f3 · outbound

This paper cites Advances in machine learning and deep learn- ing applications towards wafer map defect recognition and classification: a review,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Advances in machine learning and deep learn- ing applications towards wafer map defect recognition and classification: a review,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:39.103740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 104505fd-672a-476c-a352-6d54dd613e8f · outbound

This paper cites Boosted stacking ensemble machine learning method for wafer map pattern classification.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Boosted stacking ensemble machine learning method for wafer map pattern classification

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:39.072913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6624b7e6-386f-4768-a622-aa9497e79926 · outbound

This paper cites Wafer map defect patterns semi-supervised classification using latent vector representation,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Wafer map defect patterns semi-supervised classification using latent vector representation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.982530Z

Source-reported events for the cited work

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Observation 53501633-47d4-4954-b0c2-41e2f240f752 · outbound

This paper cites A light-weight neural network for wafer map classification based on data augmentation,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition A light-weight neural network for wafer map classification based on data augmentation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.723842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c3bbe00c-8c7a-4424-b21b-e691a1cc01bb · outbound

This paper cites Wafer map defect pattern classifi- cation and image retrieval using convolutional neural network,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Wafer map defect pattern classifi- cation and image retrieval using convolutional neural network,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.715318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 24727eae-5745-464e-a3f4-bb8dd738f218 · outbound

This paper cites An efficient deep learning framework for mixed-type wafer map defect pattern recognition,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition An efficient deep learning framework for mixed-type wafer map defect pattern recognition,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.706169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f2dfe651-407d-4cfd-a359-28a9caa45249 · outbound

This paper cites Semi-supervised clustering methods,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Semi-supervised clustering methods,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.696148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 31c56715-9c10-42d2-9749-9d7a5016116f · outbound

This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,

Reference 9

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Source-reported events for the cited work

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Observation 84120f26-b987-449e-93b4-3cafefad26e6 · outbound

This paper cites Temporal Ensembling for Semi-Supervised Learning.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Temporal Ensembling for Semi-Supervised Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T11:08:37.734315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b31c2e47-1339-4693-b9fe-512e1472cb5e · outbound

This paper cites Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T11:08:37.737092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0ccb26f-f783-407c-9735-3dc72f633444 · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition A simple framework for contrastive learning of visual representations,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T11:08:37.740665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5ddca21-ac5b-4f97-9b0e-43cc4103e841 · outbound

This paper cites Deep learning-based wafer-map failure pattern recognition framework,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Deep learning-based wafer-map failure pattern recognition framework,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.674061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 65e6482c-6e33-41fe-92f4-5ed3eca2df59 · outbound

This paper cites Wafer map defect detection and recognition using joint local and nonlocal linear discriminant analysis,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Wafer map defect detection and recognition using joint local and nonlocal linear discriminant analysis,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.652071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 127df11e-1f25-43fb-a5c7-4ec3e6cab26c · outbound

This paper cites Active learning of convolutional neural network for cost-effective wafer map pattern classification,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Active learning of convolutional neural network for cost-effective wafer map pattern classification,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.500157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3805293c-0839-4e68-b051-f21bf04b2487 · outbound

This paper cites Learning from single-defect wafer maps to classify mixed-defect wafer maps,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Learning from single-defect wafer maps to classify mixed-defect wafer maps,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.452155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2db2f171-fc83-49cf-8340-4592d316e516 · outbound

This paper cites Efficient mixed-type wafer defect pattern recognition based on light-weight neural network,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Efficient mixed-type wafer defect pattern recognition based on light-weight neural network,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.442884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cfd223b7-528c-45cf-99d9-ca4148357d13 · outbound

This paper cites Semi-supervised imbalanced classification of wafer bin map defects using a dual-head cnn,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Semi-supervised imbalanced classification of wafer bin map defects using a dual-head cnn,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.433925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 68886941-69bb-4154-899d-3c97e62611bd · outbound

This paper cites Self-supervised learning of pretext- invariant representations,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Self-supervised learning of pretext- invariant representations,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.425072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 046dd97b-7c1a-4914-a43d-e29d73ea549b · outbound

This paper cites When wafer failure pattern classification meets few-shot learning and self-supervised learning,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition When wafer failure pattern classification meets few-shot learning and self-supervised learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.402359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 44c04fc1-51e9-4cf5-8d74-af059e598e03 · outbound

This paper cites Unsupervised representation learning for large-scale wafer maps in micro-electronic manufacturing,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Unsupervised representation learning for large-scale wafer maps in micro-electronic manufacturing,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.374280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4467ad15-1c38-4350-8e18-5b8db460cba6 · outbound

This paper cites Multiple granularities generative adversarial network for recognition of wafer map defects,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Multiple granularities generative adversarial network for recognition of wafer map defects,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.179063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e63d9f68-79c8-4b73-a5d6-3e84160b624e · outbound

This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Understanding contrastive representation learning through alignment and uniformity on the hypersphere,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.105282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fa18ef03-eeac-4df2-a60c-c4c0c4ce0cbc · outbound

This paper cites Contrastive representation learning: A framework and review,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Contrastive representation learning: A framework and review,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.094469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 21414c1e-1314-4971-9b16-21d32d9e3637 · outbound

This paper cites Contrastive Learning with Hard Negative Samples.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Contrastive Learning with Hard Negative Samples

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T11:08:38.008082Z

Source-reported events for the cited work

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Observation 0ab2344e-b813-42c0-ab6e-985ec1034940 · outbound

This paper cites Understanding the behaviour of contrastive loss,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Understanding the behaviour of contrastive loss,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T11:08:38.018213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ff23091-d673-466a-921a-041813b13e5f · outbound

This paper cites Supervised contrastive learn- ing,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Supervised contrastive learn- ing,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T11:08:38.021911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ec16af5-8314-4f1d-8654-589741394733 · outbound

This paper cites Wafer map failure pattern recognition and similarity ranking for large-scale data sets,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Wafer map failure pattern recognition and similarity ranking for large-scale data sets,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:08:38.075899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 442a735f-f67c-48c1-8f54-2f15099bc067 · outbound

This paper cites Smote: synthetic minority over-sampling technique,.

Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition Smote: synthetic minority over-sampling technique,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T11:08:38.030615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

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