Paper Citation Record · LEDGER

A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2301.05712.

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

pith.paper-citation-record.v1
2301.05712 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:19:51.440058Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

22
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9f6fea67-fcb3-47d0-bd00-67c35adadef5 · inbound

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art cites this paper.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T04:19:51.440058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41d80f19-94e3-4467-9a74-577a838332ea · inbound

Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting cites this paper.

Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T11:00:56.588139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdcf0b11-7f40-4fca-8eb1-62441bd1ade1 · inbound

Flippi: End To End GenAI Assistant for E-Commerce cites this paper.

Flippi: End To End GenAI Assistant for E-Commerce A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T19:23:04.340168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e329062d-8bfc-4ce7-abfa-2b3fae1d1bfe · inbound

CIG-MAE: Cross-Modal Information-Guided Masked Autoencoder for Self-Supervised WiFi Sensing cites this paper.

CIG-MAE: Cross-Modal Information-Guided Masked Autoencoder for Self-Supervised WiFi Sensing A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T18:38:19.171415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a9db1d3-243e-4cf6-b261-8168651e0c3c · inbound

Optimizing Deep Learning Photometric Redshifts for the Roman Space Telescope with HST/CANDELS cites this paper.

Optimizing Deep Learning Photometric Redshifts for the Roman Space Telescope with HST/CANDELS A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-05-16T02:27:09.098199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 641150fd-9a2d-4b0b-92ab-a17cb3e970f9 · inbound

MLT-Dedup: Efficient Large-Scale Online Video Deduplication via Multi-Level Representations and Spatial-Temporal Matching cites this paper.

MLT-Dedup: Efficient Large-Scale Online Video Deduplication via Multi-Level Representations and Spatial-Temporal Matching A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:08:02.914751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.