Paper Citation Record · LEDGER

Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2102.03065.

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

pith.paper-citation-record.v1
2102.03065 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:45:11.016776Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T20:52:58.574938Z

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 7b23e13b-279c-43cc-85b6-5e5662dd71fc · inbound

Enhancing Ambiguous Dynamic Facial Expression Recognition with Soft Label-based Data Augmentation cites this paper.

Enhancing Ambiguous Dynamic Facial Expression Recognition with Soft Label-based Data Augmentation Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:11.016776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffa98104-ee76-41f3-bb05-5fe8184de73f · inbound

TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation cites this paper.

TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T20:37:43.334821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2706b63-2e53-4f15-9538-54c58aed9dab · inbound

Model-Agnostic Meta Learning for Class Imbalance Adaptation cites this paper.

Model-Agnostic Meta Learning for Class Imbalance Adaptation Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity

Reference 49

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T11:35:19.282628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 87287186-76aa-436b-b041-37da860427c6 · inbound

Medical Model Synthesis Architectures: A Case Study cites this paper.

Medical Model Synthesis Architectures: A Case Study Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity

Reference 187

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:21:25.050380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 06148b75-29d6-4018-b8b6-a738e203c4b2 · inbound

LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection cites this paper.

LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:32:06.383534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 27e17ead-f868-4374-8b03-c73b461e6561 · inbound

LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection cites this paper.

LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:52:58.578472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.