Paper Citation Record · LEDGER

Supercm: Revisiting Clustering for Semi-Supervised Learning

As of 10 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2506.23824.

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

pith.paper-citation-record.v1
2506.23824 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:36:01.441208Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:35:59.223939Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T21:36:01.598553Z

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy23
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 73435cfc-5001-4d66-b6d0-fffa1324b8b8 · outbound

This paper cites Supercm: Revisiting Clustering for Semi-Supervised Learning.

Supercm: Revisiting Clustering for Semi-Supervised Learning Supercm: Revisiting Clustering for Semi-Supervised Learning

Reference 1

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Observation 68429964-3b88-4a8c-9e25-ecf6b8eb2d44 · outbound

This paper cites For a more extensive survey the interested reader is referred to [3, 12].

Supercm: Revisiting Clustering for Semi-Supervised Learning For a more extensive survey the interested reader is referred to [3, 12]

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:06.103877Z

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Observation 60d9e912-dacf-47de-8f4d-01d9dcafc732 · outbound

This paper cites Clustering Module As the key building block of our SSL approach, we first de- scribe the CM introduced in [13].

Supercm: Revisiting Clustering for Semi-Supervised Learning Clustering Module As the key building block of our SSL approach, we first de- scribe the CM introduced in [13]

Reference 3

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Observation 505a34eb-dc7a-4f69-b50a-461abd7931b0 · outbound

This paper cites We follow the recommendations of [17] for data pre- possessing, model architecture, and training protocol.

Supercm: Revisiting Clustering for Semi-Supervised Learning We follow the recommendations of [17] for data pre- possessing, model architecture, and training protocol

Reference 4

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Observation 09b5d286-fff0-4a24-a64d-6d9b40cbb351 · outbound

This paper cites an unresolved cited work.

Supercm: Revisiting Clustering for Semi-Supervised Learning Unresolved cited work

Reference 5

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Observation a28800bd-e138-4060-a832-aa435a386706 · outbound

This paper cites Our training strategy benefits from the built-in cluster- ing capability of the CM module and does not rely on com- plex training schemes.

Supercm: Revisiting Clustering for Semi-Supervised Learning Our training strategy benefits from the built-in cluster- ing capability of the CM module and does not rely on com- plex training schemes

Reference 6

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Observation 922a8856-b489-46ad-99d2-730d6e7913d4 · outbound

This paper cites There are many consistent expla- nations of unlabeled data: Why you should average,.

Supercm: Revisiting Clustering for Semi-Supervised Learning There are many consistent expla- nations of unlabeled data: Why you should average,

Reference 7

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Observation 8fa6754c-bf19-4b67-b8a3-38b78ac62b2f · outbound

This paper cites Preparing medical imaging data for machine learning,.

Supercm: Revisiting Clustering for Semi-Supervised Learning Preparing medical imaging data for machine learning,

Reference 8

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Observation b9af9dfc-7d4b-423f-b8bd-f2efb0ca0def · outbound

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Supercm: Revisiting Clustering for Semi-Supervised Learning Unresolved cited work

Reference 9

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Source-reported events for the cited work

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Observation db49320c-6549-481e-8f94-4e2ada2b8824 · outbound

This paper cites A Survey on Deep Semi-supervised Learning.

Supercm: Revisiting Clustering for Semi-Supervised Learning A Survey on Deep Semi-supervised Learning

Reference 10

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Source-reported events for the cited work

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Observation ff01464d-80c5-412c-a46d-12dfd3207093 · outbound

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

Supercm: Revisiting Clustering for Semi-Supervised Learning Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,

Reference 11

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Observation a88e8672-930e-4b7f-8e2b-54afdceb3912 · outbound

This paper cites Temporal ensembling for semi-supervised learning,.

Supercm: Revisiting Clustering for Semi-Supervised Learning Temporal ensembling for semi-supervised learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:04.461719Z

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Observation 9da1ecc6-7e55-438f-89ce-dc386edde775 · outbound

This paper cites Virtual adversarial training: A reg- ularization method for supervised and semi-supervised learning,.

Supercm: Revisiting Clustering for Semi-Supervised Learning Virtual adversarial training: A reg- ularization method for supervised and semi-supervised learning,

Reference 13

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Observation 7410d45a-1970-4b88-931d-f59e0f63ecbd · outbound

This paper cites Unsu- pervised deep embedding for clustering analysis,.

Supercm: Revisiting Clustering for Semi-Supervised Learning Unsu- pervised deep embedding for clustering analysis,

Reference 14

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Observation 36e52401-d540-45e0-9590-faea2e25d9ec · outbound

This paper cites Semi-supervised learning by entropy minimization,.

Supercm: Revisiting Clustering for Semi-Supervised Learning Semi-supervised learning by entropy minimization,

Reference 15

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Observation 094be1a8-2525-4958-8bcf-bff9e1f262bd · outbound

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

Supercm: Revisiting Clustering for Semi-Supervised Learning Pseudo-label : The simple and effi- cient semi-supervised learning method for deep neural networks,

Reference 16

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Observation 9325e847-a13e-4bd2-9ddd-1e52384b769e · outbound

This paper cites Meta pseudo labels,.

Supercm: Revisiting Clustering for Semi-Supervised Learning Meta pseudo labels,

Reference 17

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Observation bd454507-fc7a-4a80-a9e8-883597f24565 · outbound

This paper cites S4l: Self-supervised semi-supervised learning,.

Supercm: Revisiting Clustering for Semi-Supervised Learning S4l: Self-supervised semi-supervised learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:03.371817Z

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Observation b64d956f-a81b-4dc4-864c-4a47212f920d · outbound

This paper cites A Comprehensive Survey on Deep Clus- tering: Taxonomy, Challenges, and Future Directions,.

Supercm: Revisiting Clustering for Semi-Supervised Learning A Comprehensive Survey on Deep Clus- tering: Taxonomy, Challenges, and Future Directions,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:03.164849Z

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Observation f0e527b7-c329-4e8d-b2dc-3dc13376936b · outbound

This paper cites Joint optimization of an autoencoder for clustering and embedding,.

Supercm: Revisiting Clustering for Semi-Supervised Learning Joint optimization of an autoencoder for clustering and embedding,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:03.024985Z

Source-reported events for the cited work

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Observation 06a2c5c5-413d-441b-b4e2-62bb814b2045 · outbound

This paper cites Aver- aging weights leads to wider optima and better general- ization,.

Supercm: Revisiting Clustering for Semi-Supervised Learning Aver- aging weights leads to wider optima and better general- ization,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:01.811997Z

Source-reported events for the cited work

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Observation 2f346247-e0d5-42b5-8d5f-46f8cb780de2 · outbound

This paper cites Deep clustering for unsupervised learning of visual features,.

Supercm: Revisiting Clustering for Semi-Supervised Learning Deep clustering for unsupervised learning of visual features,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:02.649608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8d52171f-c2c6-426e-be37-956f228df376 · outbound

This paper cites Prototypical contrastive learning of unsupervised rep- resentations,.

Supercm: Revisiting Clustering for Semi-Supervised Learning Prototypical contrastive learning of unsupervised rep- resentations,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:02.498309Z

Source-reported events for the cited work

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Observation e144a072-5455-4fd7-8174-e0a60555aac9 · outbound

This paper cites Realistic evaluation of deep semi-supervised learning algorithms,.

Supercm: Revisiting Clustering for Semi-Supervised Learning Realistic evaluation of deep semi-supervised learning algorithms,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:02.329910Z

Source-reported events for the cited work

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Observation f4c434fe-a2d0-4bd6-ba02-9b788bf66a67 · outbound

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

Supercm: Revisiting Clustering for Semi-Supervised Learning Learning multiple layers of features from tiny images,

Reference 25

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Observation 842c15b7-22b6-4485-8a3c-f7cb5a644a1c · outbound

This paper cites Wide resid- ual networks,.

Supercm: Revisiting Clustering for Semi-Supervised Learning Wide resid- ual networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:02.104962Z

Source-reported events for the cited work

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Observation 42cbd1c8-d76f-47fe-9247-cc1935389337 · outbound

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

Supercm: Revisiting Clustering for Semi-Supervised Learning Adam: A method for stochastic optimization,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:01.959065Z

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Observation de3b90c7-e0e1-4c83-a8d0-a8587825aac6 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Supercm: Revisiting Clustering for Semi-Supervised Learning UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 29

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Source-reported events for the cited work

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Observation 86575c3a-b29b-4d1f-a27d-6dc390adaf82 · outbound

This paper cites The hyper-parameters β and δ are tuned over the validation dataset.

Supercm: Revisiting Clustering for Semi-Supervised Learning The hyper-parameters β and δ are tuned over the validation dataset

Reference 100

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Source-reported events for the cited work

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Pith citing papers

Observation 73435cfc-5001-4d66-b6d0-fffa1324b8b8 · inbound