Paper Citation Record · LEDGER

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training

As of 11 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2501.04527.

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

pith.paper-citation-record.v1
2501.04527 v1

Coverage vector

measured 39 of 39 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-10T21:36:58.019399Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation 3ca4b8ce-0dc2-4d0b-ae64-7865aa626368 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Explaining and Harnessing Adversarial Examples

Reference 1

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This paper cites Intriguing properties of neural networks.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Intriguing properties of neural networks

Reference 2

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This paper cites In: 2015 IEEE International Conference on Computer Vision (ICCV) (2015).

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training In: 2015 IEEE International Conference on Computer Vision (ICCV) (2015)

Reference 3

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This paper cites Pattern Recogni- tion, 107332 (2021) https://doi.org/10.1016/ j.patcog.2020.107332.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Pattern Recogni- tion, 107332 (2021) https://doi.org/10.1016/ j.patcog.2020.107332

Reference 4

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This paper cites Fooling a Real Car with Adversarial Traffic Signs.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Fooling a Real Car with Adversarial Traffic Signs

Reference 5

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This paper cites In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (2016).

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (2016)

Reference 6

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

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 7

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This paper cites In: 2016 IEEE Sym- posium on Security and Privacy (SP) (2016).

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training In: 2016 IEEE Sym- posium on Security and Privacy (SP) (2016)

Reference 8

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This paper cites International Conference on Learning Representations,International Conference on Learning Representations (2018).

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training International Conference on Learning Representations,International Conference on Learning Representations (2018)

Reference 9

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This paper cites In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019).

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

Reference 10

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Observation 739abff9-3bb6-46cd-ae4a-e42d3c06d82f · outbound

This paper cites In: Proceed- ings 2018 Network and Distributed System Security Symposium (2018).

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training In: Proceed- ings 2018 Network and Distributed System Security Symposium (2018)

Reference 11

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This paper cites Advanced Intelligent Systems, 2300658 (2024).

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Advanced Intelligent Systems, 2300658 (2024)

Reference 12

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This paper cites International Conference on Machine Learn- ing,International Conference on Machine Learning (2019).

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training International Conference on Machine Learn- ing,International Conference on Machine Learning (2019)

Reference 13

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This paper cites In: NeurIPS 2020 Workshop on Pre-registration in Machine Learning, pp.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training In: NeurIPS 2020 Workshop on Pre-registration in Machine Learning, pp

Reference 14

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This paper cites Analysis and Applications of Class-wise Robustness in Adversarial Training.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Analysis and Applications of Class-wise Robustness in Adversarial Training

Reference 15

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This paper cites In: International Confer- ence on Machine Learning, pp.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training In: International Confer- ence on Machine Learning, pp

Reference 16

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This paper cites In: Pro- ceedings of the AAAI Conference on Artificial Intelligence, vol.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training In: Pro- ceedings of the AAAI Conference on Artificial Intelligence, vol

Reference 17

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This paper cites Revisiting adversarial training for the worst-performing class.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Revisiting adversarial training for the worst-performing class

Reference 18

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Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training In: Proceedings of the AAAI Conference on Arti- ficial Intelligence, vol

Reference 19

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This paper cites In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

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This paper cites Distributionally Robust Optimization: A Review.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Distributionally Robust Optimization: A Review

Reference 21

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This paper cites International Conference on Learning Representations,International Conference on Learning Representations (2018).

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training International Conference on Learning Representations,International Conference on Learning Representations (2018)

Reference 22

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Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training robustness

Reference 23

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This paper cites Overfitting in adversarially robust deep learning.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Overfitting in adversarially robust deep learning

Reference 24

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This paper cites Jour- nal of Computer and System Sciences, 119–139 (1997) https://doi.org/10.1006/jcss.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Jour- nal of Computer and System Sciences, 119–139 (1997) https://doi.org/10.1006/jcss

Reference 25

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Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 26

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This paper cites The Journal of Risk, 21–41 (2016) https://doi.org/10.21314/ jor.2000.038.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training The Journal of Risk, 21–41 (2016) https://doi.org/10.21314/ jor.2000.038

Reference 27

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This paper cites SIAM Journal on Computing, 48–77 (2002) https://doi.org/10.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training SIAM Journal on Computing, 48–77 (2002) https://doi.org/10

Reference 28

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This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 29

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Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Nature Machine Intelli- gence, 665–673 (2020) https://doi.org/10

Reference 30

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This paper cites An Online Method for A Class of Distributionally Robust Optimization with Non-Convex Objectives.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training An Online Method for A Class of Distributionally Robust Optimization with Non-Convex Objectives

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This paper cites Advances in Neural Information Processing Systems 34, 16020–16033 (2021).

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Advances in Neural Information Processing Systems 34, 16020–16033 (2021)

Reference 32

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Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Unresolved cited work

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

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training : Reading digits in natural images with unsupervised feature learning

Reference 34

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This paper cites International Conference on Artifi- cial Intelligence and Statistics,International Conference on Artificial Intelligence and Statistics (2011).

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training International Conference on Artifi- cial Intelligence and Statistics,International Conference on Artificial Intelligence and Statistics (2011)

Reference 35

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This paper cites In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016).

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

Reference 36

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This paper cites In: Procedings of the British Machine Vision Conference 2016 (2016).

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training In: Procedings of the British Machine Vision Conference 2016 (2016)

Reference 37

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This paper cites In: 2017 IEEE Symposium on Security and Privacy (SP) (2017).

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training In: 2017 IEEE Symposium on Security and Privacy (SP) (2017)

Reference 38

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This paper cites Inter- national Conference on Machine Learn- ing,International Conference on Machine Learning (2020) 16 Appendix A Proof of Theorem 1 Theorem 1.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Inter- national Conference on Machine Learn- ing,International Conference on Machine Learning (2020) 16 Appendix A Proof of Theorem 1 Theorem 1

Reference 39

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Pith citing papers

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