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Paper Citation Record · LEDGER

A Survey of the Self Supervised Learning Mechanisms for Vision Transformers

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2408.17059.

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

pith.paper-citation-record.v1
2408.17059 v6

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 2026-08-23T06:30:58.430688+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:41:01.305512Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T15:38:33.101965Z

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 1e1ece90-76e7-43e8-ae34-976c300bb339 · inbound

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery cites this paper.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery A Survey of the Self Supervised Learning Mechanisms for Vision Transformers

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-12T12:41:01.305512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T12:41:01.305512Z digest=sha256:4e0381793ba259c82ec30795c6242a2e4224c3c2753a37fe29161a161c8f6a2c

Observation 6bed391f-4c8e-4157-b57e-e39185cb6bd1 · 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 of the Self Supervised Learning Mechanisms for Vision Transformers

Reference 87

Resolution
unresolved
no resolver link, observed 2026-08-12T04:19:51.551717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T04:19:51.551717Z digest=sha256:834a65b12e241a9afbd5da4ab612434e166c73ba7e083faecb6e241748c0117f

Observation 3272e131-d7e0-4268-8225-d1be63fa710b · inbound

AutoGen Driven Multi Agent Framework for Iterative Crime Data Analysis and Prediction cites this paper.

AutoGen Driven Multi Agent Framework for Iterative Crime Data Analysis and Prediction A Survey of the Self Supervised Learning Mechanisms for Vision Transformers

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T04:07:20.282506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:07:20.282506Z digest=sha256:90085b8b57cd72fb367b9a79e29529402c2355f41700ed097bf657e588f37c76

Observation a14c89d8-cb5c-49fd-bbf6-bf4c15a93d64 · inbound

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention cites this paper.

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention A Survey of the Self Supervised Learning Mechanisms for Vision Transformers

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T23:55:43.658160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:55:43.658160Z digest=sha256:d09ec49fcf15b12462b92195763c19feb32dd84ddb73a326fb1bf6c0ede6135c

Observation 86a80528-1ace-423c-a5c9-4b261a0d7314 · inbound

Understanding Geometric Representations in Self-Supervised Vision Transformers via Subspace Intervention cites this paper.

Understanding Geometric Representations in Self-Supervised Vision Transformers via Subspace Intervention A Survey of the Self Supervised Learning Mechanisms for Vision Transformers

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-07-03T15:38:33.103852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-07-03T15:34:54.954593Z digest=sha256:c92490d122413bd46d29bf7b37ceb621e69622d26c50cadb614ff06b02a89672