Paper Citation Record · LEDGER

PyTorch Image Quality: Metrics for Image Quality Assessment

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2208.14818.

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

pith.paper-citation-record.v1
2208.14818 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:03:19.106590Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T03:37:36.109076Z

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 deea3032-b637-42e0-89e3-a5466ca653c0 · inbound

Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillation cites this paper.

Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillation PyTorch Image Quality: Metrics for Image Quality Assessment

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:31.547332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64cb0090-53a8-4693-9b09-36b3039ef540 · inbound

DCRA-Net: Attention-Enabled Reconstruction Model for Dynamic Fetal Cardiac MRI cites this paper.

DCRA-Net: Attention-Enabled Reconstruction Model for Dynamic Fetal Cardiac MRI PyTorch Image Quality: Metrics for Image Quality Assessment

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T11:34:29.392289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce5fe202-bed4-44f9-a577-241c8bbeeb9b · inbound

RBench-V: A Primary Assessment for Visual Reasoning Models with Multi-modal Outputs cites this paper.

RBench-V: A Primary Assessment for Visual Reasoning Models with Multi-modal Outputs PyTorch Image Quality: Metrics for Image Quality Assessment

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:42.743829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cf00f34-60e8-4cc9-8ba6-9226c7e91cfb · inbound

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality cites this paper.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality PyTorch Image Quality: Metrics for Image Quality Assessment

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:26.133415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3467aab9-9f51-40b4-b732-f0e450046667 · inbound

Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models cites this paper.

Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models PyTorch Image Quality: Metrics for Image Quality Assessment

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T20:03:19.106590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24c98be6-e70d-43f0-8d45-0150247a86f9 · inbound

MRD: Using Physically Based Differentiable Rendering to Probe Vision Models for 3D Scene Understanding cites this paper.

MRD: Using Physically Based Differentiable Rendering to Probe Vision Models for 3D Scene Understanding PyTorch Image Quality: Metrics for Image Quality Assessment

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T16:46:08.057026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ab62ed4-6587-42a1-ae8a-4a1de47edfd9 · inbound

Analyzing and Guiding Zero-Shot Posterior Sampling in Diffusion Models cites this paper.

Analyzing and Guiding Zero-Shot Posterior Sampling in Diffusion Models PyTorch Image Quality: Metrics for Image Quality Assessment

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-21T13:30:12.655730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b87799a4-4576-401c-aee1-f3bf45572015 · inbound

Deep Slice Interpolation for Reducing Through-Plane Anisotropy and Noise in Head CT cites this paper.

Deep Slice Interpolation for Reducing Through-Plane Anisotropy and Noise in Head CT PyTorch Image Quality: Metrics for Image Quality Assessment

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-03T03:37:36.110375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.