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Paper Citation Record · LEDGER

On Mutual Information in Contrastive Learning for Visual Representations

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2005.13149.

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

pith.paper-citation-record.v1
2005.13149 v2

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 2026-08-16T06:30:59.297886+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:26:33.395279Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T05:00:21.747777Z

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 23a30d39-5305-432c-961a-f036083e1f46 · inbound

Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning? cites this paper.

Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning? On Mutual Information in Contrastive Learning for Visual Representations

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-11T18:20:05.901676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T18:20:05.901676Z digest=sha256:b80656dc3b7230a31980c52a1dfc2f6ea287d62b2aa94207ad04988fc66ad43a

Observation 0a023761-4a6e-4050-8fdf-d0142459ba95 · inbound

An Augmentation-Aware Theory for Self-Supervised Contrastive Learning cites this paper.

An Augmentation-Aware Theory for Self-Supervised Contrastive Learning On Mutual Information in Contrastive Learning for Visual Representations

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T13:22:31.023848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:22:31.023848Z digest=sha256:d143f4fdbf1f9b78a9f10551627d331f35472cc7b6a42fd75eb39a2ed1f56295

Observation 3cf54585-4487-469a-a647-86d80f997a0e · inbound

A Simple Contrastive Framework Of Item Tokenization For Generative Recommendation cites this paper.

A Simple Contrastive Framework Of Item Tokenization For Generative Recommendation On Mutual Information in Contrastive Learning for Visual Representations

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-15T19:26:33.395279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:26:33.395279Z digest=sha256:d87d61c2f6e6564b5cb6fa332dd97e1145f2d767e49e602f951429871bf22b50

Observation d4a26781-81fc-41bd-8277-9eb3f6a35cd9 · inbound

On the Capacity of Distinguishable Synthetic Identity Generation under Face Verification cites this paper.

On the Capacity of Distinguishable Synthetic Identity Generation under Face Verification On Mutual Information in Contrastive Learning for Visual Representations

Reference 17

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T09:26:01.610726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-10T16:01:45.159530Z digest=sha256:8d943daefa383eaca4dcee1c243853bc203d7a5100be44a244d6122183513d36

Observation bf1e2d6a-407e-47a6-9603-7fb81cd02d13 · inbound

Contrast to Detect: Dynamic Graph Contrastive Regularization for Unsupervised Anomaly Detection in Multivariate Time Series cites this paper.

Contrast to Detect: Dynamic Graph Contrastive Regularization for Unsupervised Anomaly Detection in Multivariate Time Series On Mutual Information in Contrastive Learning for Visual Representations

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:00:21.750794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-25T04:59:08.338225Z digest=sha256:053f64c2ddbbc91424972f634322a585e9a990d2d1ccd55d5071c690945bc9bc