Paper Citation Record · LEDGER

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation

As of 17 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2411.17959.

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

pith.paper-citation-record.v1
2411.17959 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:43:40.311726Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

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External citation measurements

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Outbound references

Observation ca08fc3b-0812-403a-bda0-92b9087e60b5 · outbound

This paper cites Are labels required for improving adversarial robustness? Ad- vances in Neural Information Processing Systems, 32, 2019.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Are labels required for improving adversarial robustness? Ad- vances in Neural Information Processing Systems, 32, 2019

Reference 1

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Observation 42af87cb-ba14-48d8-ab10-5aaa6513d3ca · outbound

This paper cites Square attack: a query-efficient black-box adversarial attack via random search.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Square attack: a query-efficient black-box adversarial attack via random search

Reference 2

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Observation 172ac1df-100d-45b2-973d-39f3bf31ee68 · outbound

This paper cites Curriculum adver- sarial training.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Curriculum adver- sarial training

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:42.613756Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 27192130-3f53-4901-ada9-0f90ea058a77 · outbound

This paper cites Unlabeled data improves adver- sarial robustness.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Unlabeled data improves adver- sarial robustness

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:42.512311Z

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Observation 8abf1769-1b21-4a29-a381-b93a4e7444ff · outbound

This paper cites CAT: Customized Adversarial Training for Improved Robustness.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation CAT: Customized Adversarial Training for Improved Robustness

Reference 5

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Observation 41186f6e-87a7-4be1-b2ce-b0589653d6e9 · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter- free attacks.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Reliable evaluation of adversarial robustness with an ensemble of diverse parameter- free attacks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:42.411671Z

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Observation c0e3f4cc-5091-4b58-b7b9-576ae5feb342 · outbound

This paper cites Minimally distorted adversarial examples with a fast adaptive boundary attack.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Minimally distorted adversarial examples with a fast adaptive boundary attack

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:42.247194Z

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Observation 7e9f7877-92ef-4db9-986a-98d15fcf5fd6 · outbound

This paper cites Mma training: Direct input space margin maximization through adversarial training.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Mma training: Direct input space margin maximization through adversarial training

Reference 8

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Observation 2f089e38-2aa1-4f85-8a34-4d2ee531bd05 · outbound

This paper cites Robust physical-world attacks on deep learning visual classification.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Robust physical-world attacks on deep learning visual classification

Reference 9

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Observation 9509c0c2-7dde-4121-a4fc-e9276506aaa0 · outbound

This paper cites Adversarial Attacks Against Medical Deep Learning Systems.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Adversarial Attacks Against Medical Deep Learning Systems

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T11:43:40.052784Z

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Observation 6a67aa9d-0493-41df-aca0-e57dcfa87705 · outbound

This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Goodfellow, Jonathon Shlens, and Christian Szegedy

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:41.660106Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2ea101be-9ffa-4b09-b289-7fb027f46fe9 · outbound

This paper cites Adversarial examples are not bugs, they are features.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Adversarial examples are not bugs, they are features

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:41.493329Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9880b263-670e-4295-bb53-3e2b6654c0d6 · outbound

This paper cites Learning multiple layers of features from tiny images.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Learning multiple layers of features from tiny images

Reference 13

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Observation ecd1ae07-a700-43a4-82da-0be1d0b44b9d · outbound

This paper cites Adver- sarial examples in the physical world, 2017.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Adver- sarial examples in the physical world, 2017

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:41.287788Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8a7ff5a3-a10e-4e8c-b63e-7fc60f5c2cca · outbound

This paper cites Probabilistic mar- gins for instance reweighting in adversarial training.Advances in Neural Information Processing Systems, 34:23258–23269,.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Probabilistic mar- gins for instance reweighting in adversarial training.Advances in Neural Information Processing Systems, 34:23258–23269,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:41.099398Z

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Observation a48881d5-7c54-447e-ae98-0b5e46ab6497 · outbound

This paper cites Towards deep learn- ing models resistant to adversarial attacks.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Towards deep learn- ing models resistant to adversarial attacks

Reference 16

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Observation f803b4de-cdca-4f69-b734-03415ad7dc34 · outbound

This paper cites Virtual adversarial training: a regularization method for supervised and semi-supervised learning.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Virtual adversarial training: a regularization method for supervised and semi-supervised learning

Reference 17

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Observation cf8b1906-4f50-4c73-be3f-03ebfebc42ff · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Reading digits in natural images with unsupervised feature learning

Reference 18

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Source-reported events for the cited work

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Observation 6c3b9d52-cec9-4dc5-a9ae-aca746b684d2 · outbound

This paper cites Reducing excessive margin to achieve a better accuracy vs.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Reducing excessive margin to achieve a better accuracy vs

Reference 19

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Observation 560600da-d21a-482d-af9a-eca8bdfaccc4 · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:40.923643Z

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Observation d1d11270-b7ec-419d-bd30-122170283e46 · outbound

This paper cites Fooling automated surveillance cameras: adversarial patches to attack person detection.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Fooling automated surveillance cameras: adversarial patches to attack person detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:40.845790Z

Source-reported events for the cited work

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Observation d23a322f-2bc7-4712-945e-cd48a0de06a3 · outbound

This paper cites Robustness may be at odds with accuracy.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Robustness may be at odds with accuracy

Reference 22

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Observation f736001d-86b0-4b43-a6c9-da774cebbd41 · outbound

This paper cites Improving adversarial robustness requires revisiting misclassified examples.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Improving adversarial robustness requires revisiting misclassified examples

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:40.773359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 916d50b7-cfb3-4b34-8b93-8b0f23f67689 · outbound

This paper cites Adversarial driving: Attacking end-to- end autonomous driving.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Adversarial driving: Attacking end-to- end autonomous driving

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:40.748813Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6aef80ff-a710-4cf7-a28a-307d12946fe1 · outbound

This paper cites Improving adversarial robustness by putting more regularizations on less robust samples.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Improving adversarial robustness by putting more regularizations on less robust samples

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:40.721018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e5a7481b-4b27-4487-8899-6b79acdb364d · outbound

This paper cites Enhanc- ing adversarial robustness in low-label regime via adaptively weighted regularization and knowledge distillation.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Enhanc- ing adversarial robustness in low-label regime via adaptively weighted regularization and knowledge distillation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:40.667150Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 88a803e0-045e-4b98-b0e5-f842cbe93ea2 · outbound

This paper cites One size does not fit all: Data- adaptive adversarial training.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation One size does not fit all: Data- adaptive adversarial training

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:40.623576Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2de91f8d-6ba5-4aff-a0d1-7ca24d8c14bf · outbound

This paper cites Wide residual networks.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Wide residual networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:40.555928Z

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Observation c0f5c482-eef2-4002-9252-492bcd9e71e2 · outbound

This paper cites Theoretically principled trade-off between robustness and accuracy.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Theoretically principled trade-off between robustness and accuracy

Reference 29

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Observation acaebdc1-aef7-44b8-85f0-630d9458d5ea · outbound

This paper cites Attacks which do not kill training make adversarial learning stronger.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Attacks which do not kill training make adversarial learning stronger

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:40.462301Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 12de1bfb-83a2-43bf-b317-c7d0f3f96eb3 · outbound

This paper cites Geometry-aware Instance-reweighted Adversarial Training.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Geometry-aware Instance-reweighted Adversarial Training

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T11:43:40.291672Z

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Observation d7b962ac-9dc8-4e66-bd1a-4ca1696ea11e · outbound

This paper cites Curious” refers to global epsilon scheduling CURIOUS -(1.25, 70). “Const.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Curious” refers to global epsilon scheduling CURIOUS -(1.25, 70). “Const

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:40.434009Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

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