Paper Citation Record · LEDGER

Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2101.08533.

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

pith.paper-citation-record.v1
2101.08533 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:31:05.808772Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T00:03:39.395059Z

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 a0d88e03-505e-4bb4-9b7c-e51132615001 · inbound

Pose-dIVE: Pose-Diversified Augmentation with Diffusion Model for Person Re-Identification cites this paper.

Pose-dIVE: Pose-Diversified Augmentation with Diffusion Model for Person Re-Identification Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-24T00:03:39.397217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a6e1923c-3e6e-4bbd-939c-58b9a960c87b · inbound

Fusing Physics-Driven Strategies and Cross-Modal Adversarial Learning: Toward Multi-Domain Applications cites this paper.

Fusing Physics-Driven Strategies and Cross-Modal Adversarial Learning: Toward Multi-Domain Applications Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T05:31:24.184422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c62e946-8c05-4fb4-8a85-4d7b1413f1c7 · inbound

On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning cites this paper.

On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T20:31:05.808772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c9299f1-0e30-4f7f-b0e7-f53529a02d55 · inbound

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation cites this paper.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:10.541928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a59e8b0-f24f-41d5-bd05-2840cc8bee1f · inbound

Dynamic Modality Scheduling for Multimodal Large Models via Confidence, Uncertainty, and Semantic Consistency cites this paper.

Dynamic Modality Scheduling for Multimodal Large Models via Confidence, Uncertainty, and Semantic Consistency Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T00:47:50.362206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f04da94-1dda-4e8a-869b-7f4d806193fe · inbound

Cross-modal Ship Re-Identification via Optical and SAR Imagery: A Novel Dataset and Method cites this paper.

Cross-modal Ship Re-Identification via Optical and SAR Imagery: A Novel Dataset and Method Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:38.553922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e9c1c87-6de6-4054-8d2a-ff528d4fa347 · inbound

FADE: Adversarial Concept Erasure in Flow Models cites this paper.

FADE: Adversarial Concept Erasure in Flow Models Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T17:00:00.346876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 941b5629-8d82-428e-b675-ed88abbf59ef · inbound

Towards Universal Modal Tracking with Online Dense Temporal Token Learning cites this paper.

Towards Universal Modal Tracking with Online Dense Temporal Token Learning Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method

Reference 106

Resolution: unresolved; no resolver link, observed 2026-08-15T17:53:16.008412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dc80e75-3ada-4bcf-a999-76d8298b3db0 · inbound

Exploring Decoupled Spatio-Temporal Consistency Learning and Self-Prompting Evolution for Self-Supervised Tracking cites this paper.

Exploring Decoupled Spatio-Temporal Consistency Learning and Self-Prompting Evolution for Self-Supervised Tracking Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T12:41:51.272488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.