Identifying and Understanding Cross-Class Features in Adversarial Training | Paper Citation Record

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This paper cites write newline.

Identifying and Understanding Cross-Class Features in Adversarial Training write newline

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This paper cites and Flammarion, N.

Identifying and Understanding Cross-Class Features in Adversarial Training and Flammarion, N

Reference 2

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This paper cites Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples.

Identifying and Understanding Cross-Class Features in Adversarial Training Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples

Reference 3

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This paper cites Clustering effect of adversarial robust models.

Identifying and Understanding Cross-Class Features in Adversarial Training Clustering effect of adversarial robust models

Reference 4

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This paper cites Improving adversarial robustness via channel-wise activation suppressing.

Identifying and Understanding Cross-Class Features in Adversarial Training Improving adversarial robustness via channel-wise activation suppressing

Reference 5

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This paper cites Robust classification via a single diffusion model.

Identifying and Understanding Cross-Class Features in Adversarial Training Robust classification via a single diffusion model

Reference 6

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This paper cites Robust overfitting may be mitigated by properly learned smoothening.

Identifying and Understanding Cross-Class Features in Adversarial Training Robust overfitting may be mitigated by properly learned smoothening

Reference 7

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This paper cites Cat: Customized adversarial training for improved robustness.

Identifying and Understanding Cross-Class Features in Adversarial Training Cat: Customized adversarial training for improved robustness

Reference 8

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This paper cites M., Rosenfeld, E., and Kolter, J.

Identifying and Understanding Cross-Class Features in Adversarial Training M., Rosenfeld, E., and Kolter, J

Reference 9

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This paper cites Label noise in adversarial training: A novel perspective to study robust overfitting.

Identifying and Understanding Cross-Class Features in Adversarial Training Label noise in adversarial training: A novel perspective to study robust overfitting

Reference 10

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This paper cites Exploring memorization in adversarial training.

Identifying and Understanding Cross-Class Features in Adversarial Training Exploring memorization in adversarial training

Reference 11

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This paper cites On the Role of Discrete Tokenization in Visual Representation Learning.

Identifying and Understanding Cross-Class Features in Adversarial Training On the Role of Discrete Tokenization in Visual Representation Learning

Reference 12

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This paper cites A., and Mann, T.

Identifying and Understanding Cross-Class Features in Adversarial Training A., and Mann, T

Reference 14

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This paper cites ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond.

Identifying and Understanding Cross-Class Features in Adversarial Training ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond

Reference 15

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This paper cites Identity mappings in deep residual networks.

Identifying and Understanding Cross-Class Features in Adversarial Training Identity mappings in deep residual networks

Reference 16

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This paper cites Distilling the Knowledge in a Neural Network.

Identifying and Understanding Cross-Class Features in Adversarial Training Distilling the Knowledge in a Neural Network

Reference 17

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This paper cites Boosting accuracy and robustness of student models via adaptive adversarial distillation.

Identifying and Understanding Cross-Class Features in Adversarial Training Boosting accuracy and robustness of student models via adaptive adversarial distillation

Reference 18

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This paper cites M., Gu, Q., Bailey, J., and Ma, X.

Identifying and Understanding Cross-Class Features in Adversarial Training M., Gu, Q., Bailey, J., and Ma, X

Reference 19

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

Identifying and Understanding Cross-Class Features in Adversarial Training Adversarial examples are not bugs, they are features

Reference 20

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This paper cites Fantastic generalization measures and where to find them.

Identifying and Understanding Cross-Class Features in Adversarial Training Fantastic generalization measures and where to find them

Reference 21

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This paper cites Understanding catastrophic overfitting in single-step adversarial training.

Identifying and Understanding Cross-Class Features in Adversarial Training Understanding catastrophic overfitting in single-step adversarial training

Reference 22

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

Identifying and Understanding Cross-Class Features in Adversarial Training Learning multiple layers of features from tiny images

Reference 23

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Identifying and Understanding Cross-Class Features in Adversarial Training and Aila, T

Reference 24

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This paper cites Adversarial examples are not real features.

Identifying and Understanding Cross-Class Features in Adversarial Training Adversarial examples are not real features

Reference 25

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This paper cites and Li, Y.

Identifying and Understanding Cross-Class Features in Adversarial Training and Li, Y

Reference 26

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This paper cites and Spratling, M.

Identifying and Understanding Cross-Class Features in Adversarial Training and Spratling, M

Reference 27

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

Identifying and Understanding Cross-Class Features in Adversarial Training Towards deep learning models resistant to adversarial attacks

Reference 28

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Identifying and Understanding Cross-Class Features in Adversarial Training Unresolved cited work

Reference 29

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This paper cites When adversarial training meets vision transformers: Recipes from training to architecture.

Identifying and Understanding Cross-Class Features in Adversarial Training When adversarial training meets vision transformers: Recipes from training to architecture

Reference 30

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This paper cites Bag of tricks for adversarial training.

Identifying and Understanding Cross-Class Features in Adversarial Training Bag of tricks for adversarial training

Reference 31

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This paper cites Distillation as a defense to adversarial perturbations against deep neural networks.

Identifying and Understanding Cross-Class Features in Adversarial Training Distillation as a defense to adversarial perturbations against deep neural networks

Reference 32

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Identifying and Understanding Cross-Class Features in Adversarial Training A., Stimberg, F., Wiles, O., and Mann, T

Reference 33

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This paper cites Overfitting in adversarially robust deep learning.

Identifying and Understanding Cross-Class Features in Adversarial Training Overfitting in adversarially robust deep learning

Reference 34

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This paper cites R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D.

Identifying and Understanding Cross-Class Features in Adversarial Training R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D

Reference 35

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This paper cites A., Xu, Z., Dickerson, J., Studer, C., Davis, L.

Identifying and Understanding Cross-Class Features in Adversarial Training A., Xu, Z., Dickerson, J., Studer, C., Davis, L

Reference 36

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This paper cites Training data-efficient image transformers & distillation through attention.

Identifying and Understanding Cross-Class Features in Adversarial Training Training data-efficient image transformers & distillation through attention

Reference 37

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This paper cites Robustness may be at odds with accuracy.

Identifying and Understanding Cross-Class Features in Adversarial Training Robustness may be at odds with accuracy

Reference 38

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Identifying and Understanding Cross-Class Features in Adversarial Training and Wang, Y

Reference 39

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This paper cites On the convergence and robustness of adversarial training.

Identifying and Understanding Cross-Class Features in Adversarial Training On the convergence and robustness of adversarial training

Reference 40

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This paper cites Improving adversarial robustness requires revisiting misclassified examples.

Identifying and Understanding Cross-Class Features in Adversarial Training Improving adversarial robustness requires revisiting misclassified examples

Reference 41

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This paper cites Balance, imbalance, and rebalance: Understanding robust overfitting from a minimax game perspective.

Identifying and Understanding Cross-Class Features in Adversarial Training Balance, imbalance, and rebalance: Understanding robust overfitting from a minimax game perspective

Reference 42

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This paper cites Better diffusion models further improve adversarial training.

Identifying and Understanding Cross-Class Features in Adversarial Training Better diffusion models further improve adversarial training

Reference 43

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This paper cites Cfa: Class-wise calibrated fair adversarial training.

Identifying and Understanding Cross-Class Features in Adversarial Training Cfa: Class-wise calibrated fair adversarial training

Reference 44

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Identifying and Understanding Cross-Class Features in Adversarial Training Unresolved cited work

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This paper cites Adversarial weight perturbation helps robust generalization.

Identifying and Understanding Cross-Class Features in Adversarial Training Adversarial weight perturbation helps robust generalization

Reference 46

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Identifying and Understanding Cross-Class Features in Adversarial Training Annealing self-distillation rectification improves adversarial training

Reference 47

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Identifying and Understanding Cross-Class Features in Adversarial Training Robust weight perturbation for adversarial training

Reference 48

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This paper cites Understanding robust overfitting of adversarial training and beyond.

Identifying and Understanding Cross-Class Features in Adversarial Training Understanding robust overfitting of adversarial training and beyond

Reference 49

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This paper cites Revisiting adversarial robustness distillation from the perspective of robust fairness.

Identifying and Understanding Cross-Class Features in Adversarial Training Revisiting adversarial robustness distillation from the perspective of robust fairness

Reference 50

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

Identifying and Understanding Cross-Class Features in Adversarial Training Theoretically principled trade-off between robustness and accuracy

Reference 51

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This paper cites On the duality between sharpness-aware minimization and adversarial training.

Identifying and Understanding Cross-Class Features in Adversarial Training On the duality between sharpness-aware minimization and adversarial training

Reference 52

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This paper cites Reliable adversarial distillation with unreliable teachers.

Identifying and Understanding Cross-Class Features in Adversarial Training Reliable adversarial distillation with unreliable teachers

Reference 53

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This paper cites Revisiting adversarial robustness distillation: Robust soft labels make student better.

Identifying and Understanding Cross-Class Features in Adversarial Training Revisiting adversarial robustness distillation: Robust soft labels make student better

Reference 54

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This paper cites @esa (Ref.

Identifying and Understanding Cross-Class Features in Adversarial Training @esa (Ref

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Identifying and Understanding Cross-Class Features in Adversarial Training Unresolved cited work

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This paper cites Explaining and Harnessing Adversarial Examples.

Identifying and Understanding Cross-Class Features in Adversarial Training Explaining and Harnessing Adversarial Examples

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