Paper Citation Record · LEDGER

QT-DoG: Quantization-aware Training for Domain Generalization

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.06020.

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

pith.paper-citation-record.v1
2410.06020 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:50:35.138190Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:59:58.411799Z

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 27d179e0-e744-4247-95b2-327123eb62c1 · inbound

Frequency Composition for Compressed and Domain-Adaptive Neural Networks cites this paper.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks QT-DoG: Quantization-aware Training for Domain Generalization

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T13:50:35.138190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58f0ae45-55dc-43e5-a7a9-8fd31240372e · inbound

Quantization Meets OOD: Generalizable Quantization-aware Training from a Flatness Perspective cites this paper.

Quantization Meets OOD: Generalizable Quantization-aware Training from a Flatness Perspective QT-DoG: Quantization-aware Training for Domain Generalization

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T13:12:23.868785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad2a32b7-e20d-401b-8e57-1c847b27392e · inbound

Toward Multi-Domain and Long-Tailed Quantization via Feature Alignment and Scaling cites this paper.

Toward Multi-Domain and Long-Tailed Quantization via Feature Alignment and Scaling QT-DoG: Quantization-aware Training for Domain Generalization

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T06:06:41.747311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4d6bc179-ecc6-4451-be7c-23896f59c22a · inbound

Neural Network Quantization by Learning Low-Loss Subspaces cites this paper.

Neural Network Quantization by Learning Low-Loss Subspaces QT-DoG: Quantization-aware Training for Domain Generalization

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:59:58.413544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.