Paper Citation Record · LEDGER

Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2112.10740.

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

pith.paper-citation-record.v1
2112.10740 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:08:09.205503Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T19:03:21.245816Z

Reference resolution

0 of 0 outbound references displayed

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verified fuzzy0
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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 9b809046-68ed-42d5-914a-67591100a026 · inbound

DINOv2: Learning Robust Visual Features without Supervision cites this paper.

DINOv2: Learning Robust Visual Features without Supervision Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-09T04:17:20.390653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 30f8c07b-fdad-4a53-921e-6d99e4493495 · inbound

Vision Transformers Need Registers cites this paper.

Vision Transformers Need Registers Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T09:41:38.194658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation edab0a12-b559-464a-b680-b8a34155b975 · inbound

Revisiting Feature Prediction for Learning Visual Representations from Video cites this paper.

Revisiting Feature Prediction for Learning Visual Representations from Video Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

Reference 77

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T12:40:24.016843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 11b0e8b7-e7d3-4dfa-8c23-8c3ce540632f · inbound

Self-Supervised Learning for Real-World Object Detection: a Survey cites this paper.

Self-Supervised Learning for Real-World Object Detection: a Survey Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:03:21.249645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation af85d8ec-a5d6-4afb-aa07-c9e3d06d0295 · inbound

A CNN-Transformer for Classification of Longitudinal 3D MRI Images -- A Case Study on Hepatocellular Carcinoma Prediction cites this paper.

A CNN-Transformer for Classification of Longitudinal 3D MRI Images -- A Case Study on Hepatocellular Carcinoma Prediction Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T19:06:04.089007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d35b416b-db92-4753-9a98-680af2481f6b · inbound

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders cites this paper.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.205503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47874451-c77a-46ef-b375-25659d7bd3b9 · inbound

MultiTaskDeltaNet: Change Detection-based Image Segmentation for Operando ETEM with Application to Carbon Gasification Kinetics cites this paper.

MultiTaskDeltaNet: Change Detection-based Image Segmentation for Operando ETEM with Application to Carbon Gasification Kinetics Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:40.934347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b88302a-e653-41c0-a9e2-9ea8b196e149 · inbound

DIET-CP: Lightweight and Data Efficient Self Supervised Continued Pretraining cites this paper.

DIET-CP: Lightweight and Data Efficient Self Supervised Continued Pretraining Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T11:31:41.927722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21c33f00-daf4-44aa-8c1a-708bc82de2e6 · inbound

{\Phi}eat: Physically Grounded Material Feature Representation cites this paper.

{\Phi}eat: Physically Grounded Material Feature Representation Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T22:16:16.678094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c88b562d-78fa-47ec-9032-ceba5da8205d · inbound

Towards Understanding Self-Pretraining for Sequence Classification cites this paper.

Towards Understanding Self-Pretraining for Sequence Classification Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

Reference 145

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T05:33:58.938863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 065bcea7-6fdb-405c-82d2-ba0a1f4f4160 · inbound

Is Self-Pretraining really useful to improve diagnosis in medical Time Series? cites this paper.

Is Self-Pretraining really useful to improve diagnosis in medical Time Series? Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:38.518026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5627b679-612c-46bb-8a9a-0ee521e8e8ae · inbound

Is Self-Pretraining really useful to improve diagnosis in medical Time Series? cites this paper.

Is Self-Pretraining really useful to improve diagnosis in medical Time Series? Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:22.749469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.