Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2212.06138.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:16:58.473198Z
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
16
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 abb3b4de-a5fb-450c-94db-24e9993da96e · inbound
Sigmoid Loss for Language Image Pre-Training cites this paper.
Sigmoid Loss for Language Image Pre-Training CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet
Reference 16
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-16T13:05:36.499897Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8d6a7660-a7ee-4b9b-ad80-c7a1328eed98 · inbound
Frugal Incremental Generative Modeling using Variational Autoencoders cites this paper.
Frugal Incremental Generative Modeling using Variational Autoencoders CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:16:58.473198Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4f65e6a5-6f37-4eb1-8761-ff15c95fb0fe · inbound
Mitigating Data Exfiltration Attacks through Layer-Wise Learning Rate Decay Fine-Tuning cites this paper.
Mitigating Data Exfiltration Attacks through Layer-Wise Learning Rate Decay Fine-Tuning CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T18:32:29.386394Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bd765582-2d9e-4238-ad34-c05c1b48301a · inbound
FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data cites this paper.
FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T22:55:16.917691Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a0e44aff-5e29-4954-86e8-02fd360bdcd9 · inbound
Prognostic Value of Lung Ultrasound Biomarkers for Readmission Risk in Congestive Heart Failure: A Pilot Data-Driven Analysis cites this paper.
Prognostic Value of Lung Ultrasound Biomarkers for Readmission Risk in Congestive Heart Failure: A Pilot Data-Driven Analysis CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet
Reference 208
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T15:43:26.405929Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.