Paper Citation Record · LEDGER

Exploring Visual Prompting: Robustness Inheritance and Beyond

As of 19 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2506.06823.

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

pith.paper-citation-record.v1
2506.06823 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:52:38.803069Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

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

50 of 50 outbound references displayed

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External citation measurements

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Outbound references

Observation be90a55f-864d-490f-9959-274a180dbc23 · outbound

This paper cites Feature purification: How adversarial training performs robust deep learning.

Exploring Visual Prompting: Robustness Inheritance and Beyond Feature purification: How adversarial training performs robust deep learning

Reference 1

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Observation 0a91bcad-9e1b-4a6a-8e5e-861009a9bf32 · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Exploring Visual Prompting: Robustness Inheritance and Beyond Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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Observation db0546ec-d6a8-4f44-a33e-d7c485bfd0bd · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

Exploring Visual Prompting: Robustness Inheritance and Beyond BEiT: BERT Pre-Training of Image Transformers

Reference 3

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Observation dd74ce99-448e-447d-9669-7420016c4f57 · outbound

This paper cites Language models are few-shot learners.

Exploring Visual Prompting: Robustness Inheritance and Beyond Language models are few-shot learners

Reference 4

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Observation 6831fd56-50da-486c-be01-9f1636fa53cb · outbound

This paper cites Adversarial Attacks and Defences: A Survey.

Exploring Visual Prompting: Robustness Inheritance and Beyond Adversarial Attacks and Defences: A Survey

Reference 5

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Observation 40d50b67-e859-4399-b979-1357754bc7a7 · outbound

This paper cites Jacobian Adversarially Regularized Networks for Robustness.

Exploring Visual Prompting: Robustness Inheritance and Beyond Jacobian Adversarially Regularized Networks for Robustness

Reference 6

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Observation 8034c0fc-81a4-4634-9f95-e865901a49cf · outbound

This paper cites Exploring simple siamese representation learning.

Exploring Visual Prompting: Robustness Inheritance and Beyond Exploring simple siamese representation learning

Reference 7

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Observation cadcc301-bcf4-4414-b673-3106ef071c53 · outbound

This paper cites Adaptformer: Adapting vision transformers for scalable visual recognition.

Exploring Visual Prompting: Robustness Inheritance and Beyond Adaptformer: Adapting vision transformers for scalable visual recognition

Reference 8

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Observation a5b3ae90-ef93-4d66-b9dc-5936ac4a83b1 · outbound

This paper cites Visual prompting for adversarial robustness.

Exploring Visual Prompting: Robustness Inheritance and Beyond Visual prompting for adversarial robustness

Reference 9

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Observation 2cfb01bc-b9db-47cc-8ec1-bafecb2e8270 · outbound

This paper cites Understanding and improving visual prompting: A label-mapping perspective.

Exploring Visual Prompting: Robustness Inheritance and Beyond Understanding and improving visual prompting: A label-mapping perspective

Reference 10

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Observation 794c2c7f-1b34-4f6e-9166-2387587a7499 · outbound

This paper cites Describing textures in the wild.

Exploring Visual Prompting: Robustness Inheritance and Beyond Describing textures in the wild

Reference 11

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Observation f9033e02-70b0-4f1a-90d9-514792290ca9 · outbound

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

Exploring Visual Prompting: Robustness Inheritance and Beyond Minimally distorted adversarial examples with a fast adaptive boundary attack

Reference 12

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Source-reported events for the cited work

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Observation 43d8b292-2c80-4827-9446-7d2a3fc339e5 · outbound

This paper cites Robustbench: a standardized adversarial robustness benchmark.

Exploring Visual Prompting: Robustness Inheritance and Beyond Robustbench: a standardized adversarial robustness benchmark

Reference 13

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Observation 13aca353-39c5-4bb3-8f34-2ad6877b926e · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Exploring Visual Prompting: Robustness Inheritance and Beyond Imagenet: A large-scale hierarchical image database

Reference 14

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Observation f66841c1-4c74-4ee5-bfce-8872f0e7f773 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Exploring Visual Prompting: Robustness Inheritance and Beyond BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 15

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Observation 11210e21-3d78-4c0e-aaf8-3a2b923d360e · outbound

This paper cites Adversarial Reprogramming of Neural Networks.

Exploring Visual Prompting: Robustness Inheritance and Beyond Adversarial Reprogramming of Neural Networks

Reference 16

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Observation 8a8b3a19-ae4c-4d53-9d91-d7f627cddd1e · outbound

This paper cites Robustness (python library), 2019.

Exploring Visual Prompting: Robustness Inheritance and Beyond Robustness (python library), 2019

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:39.146199Z

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Observation 4979c570-c3af-424d-842c-3ac5af6a341b · outbound

This paper cites Domain-adversarial training of neural networks.

Exploring Visual Prompting: Robustness Inheritance and Beyond Domain-adversarial training of neural networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:39.137543Z

Source-reported events for the cited work

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Observation 19138e8e-205b-4429-ba2a-902df54f823e · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Exploring Visual Prompting: Robustness Inheritance and Beyond Explaining and Harnessing Adversarial Examples

Reference 19

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Observation ed2d6bc6-b511-4020-8fe2-5fd066aff014 · outbound

This paper cites Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples.

Exploring Visual Prompting: Robustness Inheritance and Beyond Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples

Reference 20

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Observation 26f8893d-0c12-48db-8c99-8eb056a57d14 · outbound

This paper cites Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

Exploring Visual Prompting: Robustness Inheritance and Beyond Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 21

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Observation dfc1e3e6-d25f-49ae-a523-4b117d11c94a · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

Exploring Visual Prompting: Robustness Inheritance and Beyond Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:39.119388Z

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Observation ea86efcd-a130-49ab-b0c8-ad247ea2dcb2 · outbound

This paper cites Universal Language Model Fine-tuning for Text Classification.

Exploring Visual Prompting: Robustness Inheritance and Beyond Universal Language Model Fine-tuning for Text Classification

Reference 23

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Observation d374bff3-bb92-4487-9f5f-809ddfdc1fda · outbound

This paper cites Black-box adversarial attacks with limited queries and information.

Exploring Visual Prompting: Robustness Inheritance and Beyond Black-box adversarial attacks with limited queries and information

Reference 24

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Observation 84fc7a6f-a6fb-413d-ba85-35d62e7e5ab9 · outbound

This paper cites Visual prompt tuning.

Exploring Visual Prompting: Robustness Inheritance and Beyond Visual prompt tuning

Reference 25

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Observation 275a57ca-23f7-47f0-adc0-2b7f0563b313 · outbound

This paper cites 3d object representations for fine-grained categorization.

Exploring Visual Prompting: Robustness Inheritance and Beyond 3d object representations for fine-grained categorization

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:39.093273Z

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Observation 96880a59-42a6-4505-bc3a-8c30d29e1d71 · outbound

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

Exploring Visual Prompting: Robustness Inheritance and Beyond Learning multiple layers of features from tiny images

Reference 27

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Observation 30b08d03-e3ac-4920-9bb1-178b7d2dc87c · outbound

This paper cites Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.

Exploring Visual Prompting: Robustness Inheritance and Beyond Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

Reference 28

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This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Exploring Visual Prompting: Robustness Inheritance and Beyond Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 29

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Observation ebaea4e3-5ff5-4ef0-bd24-31d58abf821f · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

Exploring Visual Prompting: Robustness Inheritance and Beyond Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 30

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Observation ae73ca36-baa0-4ca8-a48e-184fffb45fbe · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

Exploring Visual Prompting: Robustness Inheritance and Beyond Towards deep learning models resistant to adversarial attacks

Reference 31

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Observation 91be8d17-53ce-4ab9-88bb-82b0a6df9167 · outbound

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

Exploring Visual Prompting: Robustness Inheritance and Beyond Reading digits in natural images with unsupervised feature learning

Reference 32

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Observation c5619f0b-62c2-43bf-9433-948eede0e223 · outbound

This paper cites Automated flower classification over a large number of classes.

Exploring Visual Prompting: Robustness Inheritance and Beyond Automated flower classification over a large number of classes

Reference 33

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Observation 55817c79-a374-4664-a015-1fa79010ed1f · outbound

This paper cites Blackvip: Black-box visual prompting for robust transfer learning.

Exploring Visual Prompting: Robustness Inheritance and Beyond Blackvip: Black-box visual prompting for robust transfer learning

Reference 34

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Observation 7a740313-ed3f-41f2-807a-1727689b5f23 · outbound

This paper cites A survey on transfer learning.

Exploring Visual Prompting: Robustness Inheritance and Beyond A survey on transfer learning

Reference 35

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Observation a45d7c11-c905-4018-a03d-c5730a4c3a61 · outbound

This paper cites Robustness and accuracy could be reconcilable by (proper) definition.

Exploring Visual Prompting: Robustness Inheritance and Beyond Robustness and accuracy could be reconcilable by (proper) definition

Reference 36

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Observation 55a94cf8-14c6-4519-9826-20afc06d8fc3 · outbound

This paper cites Cats and dogs.

Exploring Visual Prompting: Robustness Inheritance and Beyond Cats and dogs

Reference 37

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Observation 6966e259-b131-4a56-b25b-c2785c9908d3 · outbound

This paper cites Do adversarially robust imagenet models transfer better? Advances in Neural Information Processing Systems , 33:3533--3545, 2020.

Exploring Visual Prompting: Robustness Inheritance and Beyond Do adversarially robust imagenet models transfer better? Advances in Neural Information Processing Systems , 33:3533--3545, 2020

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:39.016566Z

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Observation e6037660-a8ce-469a-8e8c-e754bc10d79d · outbound

This paper cites Adversarial training for free! Advances in Neural Information Processing Systems , 32, 2019.

Exploring Visual Prompting: Robustness Inheritance and Beyond Adversarial training for free! Advances in Neural Information Processing Systems , 32, 2019

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:39.006742Z

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Observation b0ed7865-3773-451e-8196-a371bc4206ef · outbound

This paper cites The german traffic sign recognition benchmark: a multi-class classification competition.

Exploring Visual Prompting: Robustness Inheritance and Beyond The german traffic sign recognition benchmark: a multi-class classification competition

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:38.997453Z

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Observation 94442439-be8b-463f-b78b-96843576c8f2 · outbound

This paper cites Adversarial training and robustness for multiple perturbations.

Exploring Visual Prompting: Robustness Inheritance and Beyond Adversarial training and robustness for multiple perturbations

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:38.987796Z

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Observation fb0976df-d0a7-4395-96e8-6f1eb14a985f · outbound

This paper cites Ensemble Adversarial Training: Attacks and Defenses.

Exploring Visual Prompting: Robustness Inheritance and Beyond Ensemble Adversarial Training: Attacks and Defenses

Reference 42

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This paper cites Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources.

Exploring Visual Prompting: Robustness Inheritance and Beyond Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:38.977835Z

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Observation b9d6e3c9-c7d1-48fb-90a7-2e6496462015 · outbound

This paper cites Robustness May Be at Odds with Accuracy.

Exploring Visual Prompting: Robustness Inheritance and Beyond Robustness May Be at Odds with Accuracy

Reference 44

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Exploring Visual Prompting: Robustness Inheritance and Beyond Bilateral adversarial training: Towards fast training of more robust models against adversarial attacks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:38.968843Z

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Observation 7e6851c5-afb0-4cac-8e71-5f8cd182370e · outbound

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Exploring Visual Prompting: Robustness Inheritance and Beyond Pytorch image models

Reference 46

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This paper cites Fast is better than free: Revisiting adversarial training.

Exploring Visual Prompting: Robustness Inheritance and Beyond Fast is better than free: Revisiting adversarial training

Reference 47

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This paper cites Conditional prompt learning for vision-language models.

Exploring Visual Prompting: Robustness Inheritance and Beyond Conditional prompt learning for vision-language models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:38.954964Z

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Observation 81db3a34-b2a6-4c2e-9d67-5434157ea235 · outbound

This paper cites Learning to prompt for vision-language models.

Exploring Visual Prompting: Robustness Inheritance and Beyond Learning to prompt for vision-language models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:38.945882Z

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Observation 47a32795-ce8b-415d-9a96-09e9d7a4e553 · outbound

This paper cites write newline.

Exploring Visual Prompting: Robustness Inheritance and Beyond write newline

Reference 50

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Pith citing papers

No inbound Pith citation observations are available.