Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2104.09425.
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 2026-08-07T05:12:28.373700Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T21:18:59.597420Z
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 cde2d331-ad70-4447-aaf3-d8429d5446ef · inbound
Towards Class-wise Fair Adversarial Training via Anti-Bias Soft Label Distillation cites this paper.
Towards Class-wise Fair Adversarial Training via Anti-Bias Soft Label Distillation Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:12:28.373700Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a1571c3f-9840-48cd-9fee-3f484376880a · inbound
Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes cites this paper.
Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T18:28:36.182405Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3ae307d8-e640-43d7-a5ee-552632d005e8 · inbound
Sample-wise Adaptive Weighting for Transfer Consistency in Adversarial Distillation cites this paper.
Sample-wise Adaptive Weighting for Transfer Consistency in Adversarial Distillation Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
Reference 60
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T23:53:43.063273Z
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 2c097312-f756-4f75-9f68-495785ee139e · inbound
A Prototypical Signature Approach for Writer-Independent Offline Signature Verification cites this paper.
A Prototypical Signature Approach for Writer-Independent Offline Signature Verification Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
Reference 296
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T21:18:59.599098Z
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 b3e4c437-9187-400f-abc8-4f71cec9ebf6 · inbound
Adversarial Frontiers: Minimum-Norm Attack Ensembles for Robustness Evaluation cites this paper.
Adversarial Frontiers: Minimum-Norm Attack Ensembles for Robustness Evaluation Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T11:41:31.621683Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.