Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2303.15127.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T23:13:47.409491Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-08T04:24:32.497204Z
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 9f358a01-80e7-43fc-ae3c-59a0cbbd6b9b · inbound
Nonlinear Transformations Against Unlearnable Datasets cites this paper.
Nonlinear Transformations Against Unlearnable Datasets Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T09:10:33.816571Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 9145cefa-e988-430b-a43d-e2bb65974845 · inbound
MTL-UE: Learning to Learn Nothing for Multi-Task Learning cites this paper.
MTL-UE: Learning to Learn Nothing for Multi-Task Learning Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:13:47.409491Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f3535c41-bcf2-429f-bd06-7c8674a2a412 · inbound
Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training cites this paper.
Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
Reference 75
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T21:46:34.349550Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 3cb843e0-0d66-425e-8633-f302a4ef0986 · inbound
Dual-branch Robust Unlearnable Examples cites this paper.
Dual-branch Robust Unlearnable Examples Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
Reference 7
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T09:50:59.352479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation c45869fe-5846-47be-9611-ab78e36592c3 · inbound
Dual-branch Robust Unlearnable Examples cites this paper.
Dual-branch Robust Unlearnable Examples Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
Reference 7
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T00:45:12.301851Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 9e0aea3f-47aa-4a7f-a25e-5da07434fca1 · inbound
Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms cites this paper.
Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
Reference 39
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T07:11:53.505897Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 44ee9afa-e816-4760-bc16-353387ec35a1 · inbound
Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Autonomous Vehicles cites this paper.
Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Autonomous Vehicles Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
Reference 16
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-08T04:24:32.499121Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 35d499f9-cdb4-457f-a034-de32e149c13a · inbound
DiffUE: Enhancing Utility-Unlearnability Trade-off of Unlearnable Examples via Diffusion Autoencoders cites this paper.
DiffUE: Enhancing Utility-Unlearnability Trade-off of Unlearnable Examples via Diffusion Autoencoders Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T10:41:43.164852Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b907b1ea-677a-40f7-89c4-d72fc6ed1405 · inbound
Reversible Unlearnable Examples: Towards the Copyright Protection in Deep Learning Era cites this paper.
Reversible Unlearnable Examples: Towards the Copyright Protection in Deep Learning Era Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:34:49.733556Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.