Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:31:05.848205Z
Paper Citation Record · LEDGER
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning
As of 16 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2505.12681.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
14 of 14 outbound references displayed
- verified exact0
- verified fuzzy8
- unresolved6
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 72085386-7162-47a8-9d14-464ac57fc3b7 · outbound
This paper cites Explaining and Harnessing Adversarial Examples.
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning Explaining and Harnessing Adversarial Examples
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:31:05.783415Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c3a40bd0-bebf-494b-bd0b-f8ab2d5a571d · outbound
This paper cites Towards deep learning models resistant to adversarial attacks.International Confer- ence on Learning Representations (ICLR), 2018.
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning Towards deep learning models resistant to adversarial attacks.International Confer- ence on Learning Representations (ICLR), 2018
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:31:06.082886Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0a1d027a-a107-4eb8-ab5d-11686b996e0e · outbound
This paper cites Theoretically prin- cipled trade-off between robustness and accuracy.
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning Theoretically prin- cipled trade-off between robustness and accuracy
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:31:06.066213Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d6c4cf68-708f-4133-8c05-507667a14516 · outbound
This paper cites Adversarial training for free! InAdvances in Neu- ral Information Processing Systems (NeurIPS), volume 32,.
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning Adversarial training for free! InAdvances in Neu- ral Information Processing Systems (NeurIPS), volume 32,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:31:06.050293Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b0baae3c-b049-4cd6-ac8f-f21f3d3f1ce2 · outbound
This paper cites Adversarial Learning for Neural PDE Solvers with Sparse Data.
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning Adversarial Learning for Neural PDE Solvers with Sparse Data
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:31:05.803294Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6c62e946-8c05-4fb4-8a85-4d7b1413f1c7 · outbound
This paper cites Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method.
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 2c012997-df1a-4d2a-a279-ee184491cd6f · outbound
This paper cites Beyond augmentation: Empowering model robustness under extreme capture environments.
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning Beyond augmentation: Empowering model robustness under extreme capture environments
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:31:06.034388Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 994e0f2c-2d8c-4047-99c6-619cf3005156 · outbound
This paper cites Beyond dropout: Robust convolutional neural networks based on local feature masking.
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning Beyond dropout: Robust convolutional neural networks based on local feature masking
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:31:06.018801Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 04f891e9-b925-4a9d-82fc-96c9a5fbed94 · outbound
This paper cites Cross-modality perturbation synergy attack for person re-identification.
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning Cross-modality perturbation synergy attack for person re-identification
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:31:06.002861Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 12627cb7-3c12-47c0-896f-6dd5774bca36 · outbound
This paper cites Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization.
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:31:05.828368Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f4b449b8-7e0a-454b-a33c-dad2d4b3edec · outbound
This paper cites Person re- identification method based on color attack and joint de- fence.
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning Person re- identification method based on color attack and joint de- fence
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:31:05.833270Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cc38dcc4-dc12-46c6-8236-8792b5fd8859 · outbound
This paper cites Person re-identification method based on grayscale feature enhance- ment.Journal of Computer Applications, 41(12):3590, 2021.
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning Person re-identification method based on grayscale feature enhance- ment.Journal of Computer Applications, 41(12):3590, 2021
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:31:05.976742Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f0e20c9c-3712-461f-8cb3-80a28c87ce70 · outbound
This paper cites Exploring Color Invariance through Image-Level Ensemble Learning.
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning Exploring Color Invariance through Image-Level Ensemble Learning
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:31:05.843023Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d2825501-c746-49c5-8b57-bf30d21eaa2e · outbound
This paper cites Cross-task attack: A self-supervision generative framework based on attention shift.
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning Cross-task attack: A self-supervision generative framework based on attention shift
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:31:05.960097Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.