Understanding Adversarial Training with Energy-based Models | Paper Citation Record

Observation 3daa1341-76a6-4fe0-8efa-e2b666690419 · outbound

This paper cites Intriguing properties of neural networks,.

Understanding Adversarial Training with Energy-based Models Intriguing properties of neural networks,

Reference 1

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Observation 575eff68-ad8c-443c-97bb-41bee41399e2 · outbound

This paper cites Explaining and harnessing adversarial examples,.

Understanding Adversarial Training with Energy-based Models Explaining and harnessing adversarial examples,

Reference 2

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Observation 289c0cc1-1926-44ad-bb02-f49f4834860f · outbound

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

Understanding Adversarial Training with Energy-based Models Towards deep learning models resistant to adversarial attacks,

Reference 3

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

Understanding Adversarial Training with Energy-based Models Theoretically principled trade-off between robustness and accuracy,

Reference 4

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This paper cites Adversarial training for free!.

Understanding Adversarial Training with Energy-based Models Adversarial training for free!

Reference 5

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Observation 4c62b045-c62b-495a-a5cd-aee97c8f778d · outbound

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

Understanding Adversarial Training with Energy-based Models Fast is better than free: Revisiting adversarial training,

Reference 6

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This paper cites Improving adversarial robustness requires revisiting misclassified examples,.

Understanding Adversarial Training with Energy-based Models Improving adversarial robustness requires revisiting misclassified examples,

Reference 7

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Observation 3acf2d8a-5b9e-4567-9ead-2a64f4f9ec30 · outbound

This paper cites Towards efficient and effective adversarial training,.

Understanding Adversarial Training with Energy-based Models Towards efficient and effective adversarial training,

Reference 8

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Observation 4644f834-29c3-48f6-9e80-b56ef1a3a69c · outbound

This paper cites Guided adversarial attack for evaluating and enhancing adversarial defenses,.

Understanding Adversarial Training with Energy-based Models Guided adversarial attack for evaluating and enhancing adversarial defenses,

Reference 9

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Observation b7ea4964-8bf4-4a0a-92c7-716c2877e959 · outbound

This paper cites Unlabeled data improves adversarial robustness,.

Understanding Adversarial Training with Energy-based Models Unlabeled data improves adversarial robustness,

Reference 10

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This paper cites Improving robustness using generated data,.

Understanding Adversarial Training with Energy-based Models Improving robustness using generated data,

Reference 11

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This paper cites Better diffusion models further improve adversarial training,.

Understanding Adversarial Training with Energy-based Models Better diffusion models further improve adversarial training,

Reference 12

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This paper cites Probabilistic margins for instance reweighting in adversarial training,.

Understanding Adversarial Training with Energy-based Models Probabilistic margins for instance reweighting in adversarial training,

Reference 13

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This paper cites Overfitting in adversarially robust deep learning,.

Understanding Adversarial Training with Energy-based Models Overfitting in adversarially robust deep learning,

Reference 14

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This paper cites Shedding more light on robust classifiers under the lens of energy- based models,.

Understanding Adversarial Training with Energy-based Models Shedding more light on robust classifiers under the lens of energy- based models,

Reference 15

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Observation c6209312-ac54-4049-97b4-747bf35ce257 · outbound

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

Understanding Adversarial Training with Energy-based Models Understanding catastrophic overfitting in single-step adversarial training,

Reference 16

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This paper cites Robustbench: a standardized adversarial robustness benchmark,.

Understanding Adversarial Training with Energy-based Models Robustbench: a standardized adversarial robustness benchmark,

Reference 17

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This paper cites Robust principles: Architectural design principles for adversarially robust CNNs,.

Understanding Adversarial Training with Energy-based Models Robust principles: Architectural design principles for adversarially robust CNNs,

Reference 18

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This paper cites Adversarial weight perturbation helps robust generalization,.

Understanding Adversarial Training with Energy-based Models Adversarial weight perturbation helps robust generalization,

Reference 19

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Observation e26de8b8-836b-42b0-982a-43de417b4992 · outbound

This paper cites Towards understanding the generative capability of adversarially robust classifiers,.

Understanding Adversarial Training with Energy-based Models Towards understanding the generative capability of adversarially robust classifiers,

Reference 20

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Observation e2d7bf46-7cc0-4b33-a6e6-6346d9762a46 · outbound

This paper cites Your classifier is secretly an energy based model and you should treat it like one,.

Understanding Adversarial Training with Energy-based Models Your classifier is secretly an energy based model and you should treat it like one,

Reference 21

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This paper cites Exploring the connection between robust and generative models,.

Understanding Adversarial Training with Energy-based Models Exploring the connection between robust and generative models,

Reference 22

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This paper cites Towards evaluating the robustness of neural networks,.

Understanding Adversarial Training with Energy-based Models Towards evaluating the robustness of neural networks,

Reference 23

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

Understanding Adversarial Training with Energy-based Models Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,

Reference 24

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This paper cites Diffusion-based adversarial sample generation for improved stealthiness and controllability,.

Understanding Adversarial Training with Energy-based Models Diffusion-based adversarial sample generation for improved stealthiness and controllability,

Reference 25

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Observation ed4902c9-d270-4172-954d-1f1d08ee2160 · outbound

This paper cites Adversarial Logit Pairing.

Understanding Adversarial Training with Energy-based Models Adversarial Logit Pairing

Reference 26

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This paper cites Eliminating catastrophic overfitting via abnormal adversarial examples regularization,.

Understanding Adversarial Training with Energy-based Models Eliminating catastrophic overfitting via abnormal adversarial examples regularization,

Reference 27

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Observation b1be3814-4cfe-498e-b83e-5b5b47414740 · outbound

This paper cites On adversarial training without perturbing all examples,.

Understanding Adversarial Training with Energy-based Models On adversarial training without perturbing all examples,

Reference 28

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This paper cites On the over-memorization during natural, robust and catastrophic overfitting,.

Understanding Adversarial Training with Energy-based Models On the over-memorization during natural, robust and catastrophic overfitting,

Reference 29

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

Understanding Adversarial Training with Energy-based Models Square attack: a query-efficient black-box adversarial attack via random search,

Reference 30

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Observation dac37fe8-b8a6-4c8b-ac4b-15a6c40c3163 · outbound

This paper cites Decoupled Kullback-Leibler Divergence Loss.

Understanding Adversarial Training with Energy-based Models Decoupled Kullback-Leibler Divergence Loss

Reference 31

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This paper cites Ensemble adversarial training: Attacks and defenses,.

Understanding Adversarial Training with Energy-based Models Ensemble adversarial training: Attacks and defenses,

Reference 32

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This paper cites Single-step adversarial training with dropout scheduling,.

Understanding Adversarial Training with Energy-based Models Single-step adversarial training with dropout scheduling,

Reference 33

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Observation e248ae70-0226-4ddf-a26e-b13ef1a83b52 · outbound

This paper cites Make some noise: Reliable and efficient single-step adversarial training,.

Understanding Adversarial Training with Energy-based Models Make some noise: Reliable and efficient single-step adversarial training,

Reference 34

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Observation 6f98ffbd-4f4e-4efa-9caf-83400634bfc8 · outbound

This paper cites Reliably fast adversarial training via latent adversarial perturbation,.

Understanding Adversarial Training with Energy-based Models Reliably fast adversarial training via latent adversarial perturbation,

Reference 35

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This paper cites Zerograd: Costless conscious remedies for catastrophic overfitting in the fgsm adversarial training,.

Understanding Adversarial Training with Energy-based Models Zerograd: Costless conscious remedies for catastrophic overfitting in the fgsm adversarial training,

Reference 36

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This paper cites Understanding and improving fast adversarial training,.

Understanding Adversarial Training with Energy-based Models Understanding and improving fast adversarial training,

Reference 37

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Observation 124cb047-a8e3-4156-af27-67e85c6144fa · outbound

This paper cites Subspace adversarial training,.

Understanding Adversarial Training with Energy-based Models Subspace adversarial training,

Reference 38

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Observation bd1fea41-ba9e-4e75-866a-c4781329fc8e · outbound

This paper cites Fast adversarial training with adaptive step size,.

Understanding Adversarial Training with Energy-based Models Fast adversarial training with adaptive step size,

Reference 39

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Observation 113d1ea3-273f-44d6-ac1d-2a65740f0c1b · outbound

This paper cites Adver- sarially robust generalization requires more data,.

Understanding Adversarial Training with Energy-based Models Adver- sarially robust generalization requires more data,

Reference 40

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This paper cites Are labels required for improving adversarial robustness?.

Understanding Adversarial Training with Energy-based Models Are labels required for improving adversarial robustness?

Reference 41

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Observation c7ece742-dbc1-4ffb-896e-423c6a1d4d79 · outbound

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

Understanding Adversarial Training with Energy-based Models Adversarially Robust Generalization Just Requires More Unlabeled Data

Reference 42

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This paper cites Denoising diffusion probabilistic models,.

Understanding Adversarial Training with Energy-based Models Denoising diffusion probabilistic models,

Reference 43

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This paper cites Exploring memorization in adversarial training,.

Understanding Adversarial Training with Energy-based Models Exploring memorization in adversarial training,

Reference 44

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Observation aa82b3b7-6a95-446c-a95c-e335ba2a3837 · outbound

This paper cites Enhancing adversarial training via reweighting optimization trajectory,.

Understanding Adversarial Training with Energy-based Models Enhancing adversarial training via reweighting optimization trajectory,

Reference 45

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Observation 8ce0a46a-f246-49b5-8b15-3ab9c510bcb5 · outbound

This paper cites Sparsity Winning Twice: Better Robust Generalization from More Efficient Training.

Understanding Adversarial Training with Energy-based Models Sparsity Winning Twice: Better Robust Generalization from More Efficient Training

Reference 46

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This paper cites Relating adversarially robust generalization to flat minima,.

Understanding Adversarial Training with Energy-based Models Relating adversarially robust generalization to flat minima,

Reference 47

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Observation 3ccdfd84-cba8-4c6f-973f-f722abaa5546 · outbound

This paper cites Low curvature activations reduce overfitting in adversarial training,.

Understanding Adversarial Training with Energy-based Models Low curvature activations reduce overfitting in adversarial training,

Reference 48

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Observation b9e4f7f6-59d9-49da-9cf3-45e858dcbee5 · outbound

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

Understanding Adversarial Training with Energy-based Models Robust overfitting may be mitigated by properly learned smoothening,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:07.177038Z

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Observation bdd7c572-e147-4396-954c-98cfc86cabb8 · outbound

This paper cites Adversarial robustness through the lens of causality,.

Understanding Adversarial Training with Energy-based Models Adversarial robustness through the lens of causality,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:06.897365Z

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Observation 5e94165f-4856-4274-b736-2d68e146d783 · outbound

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

Understanding Adversarial Training with Energy-based Models Understanding robust overfitting of adversarial training and beyond,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:06.643160Z

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Observation ce625ceb-4b40-4b4f-8d6e-d9d16fdae200 · outbound

This paper cites Jem++: Improved techniques for training jem,.

Understanding Adversarial Training with Energy-based Models Jem++: Improved techniques for training jem,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:06.484127Z

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This paper cites Sharpness-aware minimization for efficiently improving generalization,.

Understanding Adversarial Training with Energy-based Models Sharpness-aware minimization for efficiently improving generalization,

Reference 53

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Observation 889c1e7a-1aed-4e46-873d-cd95ac63ddf8 · outbound

This paper cites A unified contrastive energy- based model for understanding the generative ability of adversarial training,.

Understanding Adversarial Training with Energy-based Models A unified contrastive energy- based model for understanding the generative ability of adversarial training,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:06.040436Z

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Observation 4bdb9030-b2e2-4710-843b-abc0e6c6a13f · outbound

This paper cites Towards bridging the performance gaps of joint energy-based models,.

Understanding Adversarial Training with Energy-based Models Towards bridging the performance gaps of joint energy-based models,

Reference 55

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Observation 719aa396-7645-4929-9bf6-2b5fbf10b9ed · outbound

This paper cites M-ebm: Towards understanding the manifolds of energy-based models,.

Understanding Adversarial Training with Energy-based Models M-ebm: Towards understanding the manifolds of energy-based models,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:05.755245Z

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

Observation b4d90ab8-cc2f-4d2a-b9fd-75fecf2ec325 · outbound

This paper cites Inverting Adversarially Robust Networks for Image Synthesis.

Understanding Adversarial Training with Energy-based Models Inverting Adversarially Robust Networks for Image Synthesis

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:13:02.879218Z

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Observation af46d98a-33cc-4e6a-830b-583d9bab7405 · outbound

This paper cites MAGIC: Mask-guided image synthesis by inverting a quasi-robust classifier,.

Understanding Adversarial Training with Energy-based Models MAGIC: Mask-guided image synthesis by inverting a quasi-robust classifier,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:05.619350Z

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Observation 322ec248-6ff7-4b0f-8923-291059f0703b · outbound

This paper cites Effective single-step adversarial training with energy-based models,.

Understanding Adversarial Training with Energy-based Models Effective single-step adversarial training with energy-based models,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:05.427053Z

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Observation 767a039d-8571-4d85-acc9-1068b96f2a33 · outbound

This paper cites Geometry-aware instance-reweighted adversarial training,.

Understanding Adversarial Training with Energy-based Models Geometry-aware instance-reweighted adversarial training,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:05.232405Z

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Observation 41a057b5-d254-4099-b731-3461a6e22089 · outbound

This paper cites Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training.

Understanding Adversarial Training with Energy-based Models Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:13:02.639233Z

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Observation eb2ee494-0d44-47da-95ca-252242ccc880 · outbound

This paper cites Entropy weighted adversarial training,.

Understanding Adversarial Training with Energy-based Models Entropy weighted adversarial training,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:05.009419Z

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Observation 1189317d-a055-475f-b691-f5cfafd2c587 · outbound

This paper cites Memorization weights for instance reweighting in adversarial training,.

Understanding Adversarial Training with Energy-based Models Memorization weights for instance reweighting in adversarial training,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:04.787059Z

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Observation e9ce07d8-cb7f-47d8-bb85-83d8ee262c63 · outbound

This paper cites A tutorial on energy-based learning,.

Understanding Adversarial Training with Energy-based Models A tutorial on energy-based learning,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:04.575435Z

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Observation 3436c544-cede-4f1b-898a-cbdcd73bce69 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution,.

Understanding Adversarial Training with Energy-based Models Generative modeling by estimating gradients of the data distribution,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:04.428961Z

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Observation 0de7f175-416b-4555-b065-d266c844ebf6 · outbound

This paper cites Diffusion models beat gans on image synthesis,.

Understanding Adversarial Training with Energy-based Models Diffusion models beat gans on image synthesis,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:01.354256Z

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Observation 2a1864bf-7903-4c6b-b78c-9f8bc8fb2ae6 · outbound

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

Understanding Adversarial Training with Energy-based Models Attacks which do not kill training make adversarial learning stronger,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:04.264883Z

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Observation 831a71f1-e537-45f5-b9f0-3448329bfe00 · outbound

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

Understanding Adversarial Training with Energy-based Models Mma training: Direct input space margin maximization through adversarial training,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:04.100110Z

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Observation 16e87bc9-8b7c-4299-abe2-eb592437c6e9 · outbound

This paper cites Image quality assess- ment: From error visibility to structural similarity,.

Understanding Adversarial Training with Energy-based Models Image quality assess- ment: From error visibility to structural similarity,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:01.657710Z

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Observation d4165823-f335-4db1-ae97-095c5c612511 · outbound

This paper cites Improved techniques for training gans,.

Understanding Adversarial Training with Energy-based Models Improved techniques for training gans,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:01.760123Z

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Observation c0864371-e77e-4991-8cae-01e698fc0684 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium,.

Understanding Adversarial Training with Energy-based Models Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:03.930878Z

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

Observation c694b63b-88e8-49fa-adbf-6e6d2bb1d3a2 · outbound

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

Understanding Adversarial Training with Energy-based Models Learning multiple layers of features from tiny images,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:03.743157Z

Source-reported events for the cited work

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Observation 2563dbb0-9857-4499-9ff9-1ad999610590 · outbound

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

Understanding Adversarial Training with Energy-based Models Reading digits in natural images with unsupervised feature learning,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:03.576933Z

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

Observation c0841b46-25fe-407d-812d-59a54e72fb51 · outbound

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

Understanding Adversarial Training with Energy-based Models Imagenet: A large-scale hierarchical image database,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:03.390052Z

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Observation 14f12785-9ec6-4575-abfc-89ba73b746c5 · outbound

This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

Understanding Adversarial Training with Energy-based Models Averaging Weights Leads to Wider Optima and Better Generalization

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:02.224237Z

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Observation 5f22cb8f-8676-40b3-b2cb-3a2e4f7d30d6 · outbound

This paper cites Learning energy-based models by diffusion recovery likelihood,.

Understanding Adversarial Training with Energy-based Models Learning energy-based models by diffusion recovery likelihood,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:03.217551Z

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

Observation d8b2339d-663e-49b3-90e2-2e0c4211a1e9 · outbound

This paper cites Image synthesis with a single (robust) classifier,.

Understanding Adversarial Training with Energy-based Models Image synthesis with a single (robust) classifier,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:03.059743Z

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