Paper Citation Record · LEDGER

Boosting Adversarial Robustness and Generalization with Structural Prior

As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2502.00834.

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

pith.paper-citation-record.v1
2502.00834 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:40:55.197169Z

measured 34 of 34 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.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

34 of 34 outbound references displayed

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External citation measurements

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Outbound references

Observation 9f98af91-1b9a-4582-92b4-a73772ec3f6f · outbound

This paper cites • Step 2 (Forward): input x′ as z∗(0) into model to obtain a series of hidden codes for each layer{z(l)}L l=1 by optimizing dictionary learning loss in Eq.

Boosting Adversarial Robustness and Generalization with Structural Prior • Step 2 (Forward): input x′ as z∗(0) into model to obtain a series of hidden codes for each layer{z(l)}L l=1 by optimizing dictionary learning loss in Eq

Reference 1

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Observation 7ff5a9c0-2136-411a-acb4-4ffc1782b08c · outbound

This paper cites RobustBench: a standardized adversarial robustness benchmark.

Boosting Adversarial Robustness and Generalization with Structural Prior RobustBench: a standardized adversarial robustness benchmark

Reference 2

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Observation fd9d865b-7b69-4cba-bed4-ecf43ed66498 · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

Boosting Adversarial Robustness and Generalization with Structural Prior Improved Regularization of Convolutional Neural Networks with Cutout

Reference 3

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Observation a7d4d72b-f8fb-47cb-81aa-3198fa5b186c · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Boosting Adversarial Robustness and Generalization with Structural Prior Explaining and Harnessing Adversarial Examples

Reference 5

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Observation c74c25d1-1ccf-44f7-9aa5-c9924f0eed43 · outbound

This paper cites Dynamic Label Adversarial Training for Deep Learning Robustness Against Adversarial Attacks.

Boosting Adversarial Robustness and Generalization with Structural Prior Dynamic Label Adversarial Training for Deep Learning Robustness Against Adversarial Attacks

Reference 9

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Observation b899d144-93ff-4830-9948-36f1818ac93e · outbound

This paper cites 13 Submission and Formatting Instructions for ICML 2024 B.

Boosting Adversarial Robustness and Generalization with Structural Prior 13 Submission and Formatting Instructions for ICML 2024 B

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:40:55.983210Z

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Observation bc2171c0-cbd0-430c-818d-db14636805d0 · outbound

This paper cites 19 Submission and Formatting Instructions for ICML 2024 D.2.

Boosting Adversarial Robustness and Generalization with Structural Prior 19 Submission and Formatting Instructions for ICML 2024 D.2

Reference 11

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Observation c2d741fc-ebb9-40d9-8b96-4233e9b57cc6 · outbound

This paper cites During the 100th to 150th epochs, the model experiences a catastrophic robust overfitting problem.

Boosting Adversarial Robustness and Generalization with Structural Prior During the 100th to 150th epochs, the model experiences a catastrophic robust overfitting problem

Reference 12

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Observation ad415672-37ce-466e-9ac6-f36180bfc052 · outbound

This paper cites 21 Submission and Formatting Instructions for ICML 2024 D.2.2.

Boosting Adversarial Robustness and Generalization with Structural Prior 21 Submission and Formatting Instructions for ICML 2024 D.2.2

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:40:55.881195Z

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Observation 7ab81c20-28a6-40ed-b5cd-b03d1dc1ddf0 · outbound

This paper cites 22 Submission and Formatting Instructions for ICML 2024 D.3.

Boosting Adversarial Robustness and Generalization with Structural Prior 22 Submission and Formatting Instructions for ICML 2024 D.3

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:40:55.858007Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2f3663e2-bfb2-4a39-b759-bb67fdf9e053 · outbound

This paper cites Online Adversarial Purification based on Self-Supervision.

Boosting Adversarial Robustness and Generalization with Structural Prior Online Adversarial Purification based on Self-Supervision

Reference 15

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Observation 6f44275e-f958-48fb-97d3-568835e1c407 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.

Boosting Adversarial Robustness and Generalization with Structural Prior Dropout: a simple way to prevent neural networks from overfitting

Reference 16

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Observation 65d4298e-8062-490f-8deb-4d52156b898f · outbound

This paper cites Robust sparse coding for face recognition.

Boosting Adversarial Robustness and Generalization with Structural Prior Robust sparse coding for face recognition

Reference 18

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Observation 648b74fc-92c0-4a50-aeda-2cf84a6eca1d · outbound

This paper cites Adversarially Robust Generalization Just Requires More Unlabeled Data.

Boosting Adversarial Robustness and Generalization with Structural Prior Adversarially Robust Generalization Just Requires More Unlabeled Data

Reference 19

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Observation ad787ede-cf52-4352-b99f-21802017e575 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Boosting Adversarial Robustness and Generalization with Structural Prior mixup: Beyond Empirical Risk Minimization

Reference 20

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Observation 3af0dca4-597f-4179-82f9-7e5e6107fb0c · outbound

This paper cites Background subtrac- tion via robust dictionary learning.

Boosting Adversarial Robustness and Generalization with Structural Prior Background subtrac- tion via robust dictionary learning

Reference 21

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Observation d1512655-06c5-476c-b418-0ad2ea739ec9 · outbound

This paper cites Overview of Elastic Dictionary Learning Overview of Elastic DL neural networks.

Boosting Adversarial Robustness and Generalization with Structural Prior Overview of Elastic Dictionary Learning Overview of Elastic DL neural networks

Reference 22

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Observation 2fa73463-689e-4400-856d-c8bf33fe34c5 · outbound

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Boosting Adversarial Robustness and Generalization with Structural Prior Unresolved cited work

Reference 25

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Boosting Adversarial Robustness and Generalization with Structural Prior Unresolved cited work

Reference 26

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Boosting Adversarial Robustness and Generalization with Structural Prior Unresolved cited work

Reference 31

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Boosting Adversarial Robustness and Generalization with Structural Prior Unresolved cited work

Reference 32

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Boosting Adversarial Robustness and Generalization with Structural Prior Unresolved cited work

Reference 33

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Observation 86d2b0f1-0244-40b7-80ff-6e23bdee95ac · outbound

This paper cites R ECONSTRUCTION PROCESS Image & noise reconstruction.

Boosting Adversarial Robustness and Generalization with Structural Prior R ECONSTRUCTION PROCESS Image & noise reconstruction

Reference 34

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Observation a90b2ebe-0f87-41f9-9ab6-9184b3d04c50 · outbound

This paper cites B., and Swami, A.

Boosting Adversarial Robustness and Generalization with Structural Prior B., and Swami, A

Reference 1996

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Observation 9d1393c8-b4a2-4212-9c62-8718d6e76b20 · outbound

This paper cites Crafting papers on machine learning.

Boosting Adversarial Robustness and Generalization with Structural Prior Crafting papers on machine learning

Reference 2009

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This paper cites doi: 10.1109/TIT.2010.

Boosting Adversarial Robustness and Generalization with Structural Prior doi: 10.1109/TIT.2010

Reference 2010

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Observation 9ab4dd4c-806f-4d03-a639-d66c1f5fdad6 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Boosting Adversarial Robustness and Generalization with Structural Prior Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 2013

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This paper cites Diffusion Models for Adversarial Purification.

Boosting Adversarial Robustness and Generalization with Structural Prior Diffusion Models for Adversarial Purification

Reference 2016

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Observation e6439dbf-6916-4459-b3fc-0e3d8d595b3b · outbound

This paper cites Detecting Adversarial Samples from Artifacts.

Boosting Adversarial Robustness and Generalization with Structural Prior Detecting Adversarial Samples from Artifacts

Reference 2017

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This paper cites Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?.

Boosting Adversarial Robustness and Generalization with Structural Prior Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?

Reference 2018

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Observation a98ddda4-f435-4744-8ef1-bba148e59faa · outbound

This paper cites On Detecting Adversarial Perturbations.

Boosting Adversarial Robustness and Generalization with Structural Prior On Detecting Adversarial Perturbations

Reference 2019

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This paper cites and Wagner, D.

Boosting Adversarial Robustness and Generalization with Structural Prior and Wagner, D

Reference 2020

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This paper cites On the (Statistical) Detection of Adversarial Examples.

Boosting Adversarial Robustness and Generalization with Structural Prior On the (Statistical) Detection of Adversarial Examples

Reference 2021

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Observation 00dcbe97-36d4-4372-963c-2ef90567ee1c · outbound

This paper cites Robust Graph Neural Networks via Unbiased Aggregation.

Boosting Adversarial Robustness and Generalization with Structural Prior Robust Graph Neural Networks via Unbiased Aggregation

Reference 2022

Resolution: verified exact; local_arxiv, observed 2026-08-09T17:40:55.604989Z

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Pith citing papers

No inbound Pith citation observations are available.