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Paper Citation Record · LEDGER

Self-training with Noisy Student improves ImageNet classification

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1911.04252.

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

pith.paper-citation-record.v1
1911.04252 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:35:00.452635Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T07:09:38.815892Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 873d0639-3ea1-42a6-9779-70aaa38b6784 · inbound

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP cites this paper.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Self-training with Noisy Student improves ImageNet classification

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T14:35:00.452635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:35:00.452635Z digest=sha256:e0239aaef97e2a328921d3a7b8fb422df940b499f10eac5531dfe69eb8ab9310

Observation 1383c7be-098e-494d-b386-420219e27e42 · inbound

ATMS-KD: Adaptive Temperature and Mixed Sample Knowledge Distillation for a Lightweight Residual CNN in Agricultural Embedded Systems cites this paper.

ATMS-KD: Adaptive Temperature and Mixed Sample Knowledge Distillation for a Lightweight Residual CNN in Agricultural Embedded Systems Self-training with Noisy Student improves ImageNet classification

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-05T15:17:52.407598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:17:52.407598Z digest=sha256:dbfe5ab680225c4ef3626ceb200e5a1b0c0d8bb9e8f6f8e7edc6ab787cbfd906

Observation 11799de6-8e27-453a-92f4-d13f1bc23230 · inbound

Image Recognition with Vision and Language Embeddings of VLMs cites this paper.

Image Recognition with Vision and Language Embeddings of VLMs Self-training with Noisy Student improves ImageNet classification

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-04T19:24:21.529702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T19:24:21.529702Z digest=sha256:b057b05350cd8a26dadf307aa128169d4d3d4140b7a5f22df41fcd7d5b38bd0b

Observation 01208613-035f-4123-908c-8e0dcd0edda3 · inbound

Collaborative Large and Small Language Models for Accurate and Scalable Data Repair cites this paper.

Collaborative Large and Small Language Models for Accurate and Scalable Data Repair Self-training with Noisy Student improves ImageNet classification

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-07-03T23:19:04.249568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T22:25:39.533017Z digest=sha256:6cc3659e8a13687a1dfb467fed491ff7904f2b65052ac7346a9450d704ecdbae

Observation c3b7eec4-88eb-4bbd-9a98-73fd8d9990cf · inbound

Enhancing Stateful Detection of Adversarial Attacks with Soft-labels' Temporality and Robust Similarity Approximations cites this paper.

Enhancing Stateful Detection of Adversarial Attacks with Soft-labels' Temporality and Robust Similarity Approximations Self-training with Noisy Student improves ImageNet classification

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-07-04T07:09:38.817279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T13:34:32.092622Z digest=sha256:2dbb781198cefd4a8266caba5cd63223c83185af69d0c26bbedbd41474f50cef