BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation | Paper Citation Record

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This paper cites Towards evaluating the robustness of neural networks.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Towards evaluating the robustness of neural networks

Reference 1

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This paper cites Dataset distillation by matching training trajectories.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Dataset distillation by matching training trajectories

Reference 2

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This paper cites A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness

Reference 3

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

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Reliable evalua- tion of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 4

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This paper cites Dc- bench: Dataset condensation benchmark.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Dc- bench: Dataset condensation benchmark

Reference 5

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This paper cites Scaling up dataset distillation to imagenet-1k with constant memory.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Scaling up dataset distillation to imagenet-1k with constant memory

Reference 6

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This paper cites Multirobustbench: Benchmarking robustness against multiple attacks.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Multirobustbench: Benchmarking robustness against multiple attacks

Reference 7

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

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Imagenet: A large-scale hierarchical image database

Reference 8

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Observation 5e77f51f-d356-4caf-a26d-a1585c9631ae · outbound

This paper cites Minimizing the accumulated trajectory er- ror to improve dataset distillation.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Minimizing the accumulated trajectory er- ror to improve dataset distillation

Reference 9

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This paper cites Adversarially robust distillation.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Adversarially robust distillation

Reference 10

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This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Goodfellow, Jonathon Shlens, and Christian Szegedy

Reference 11

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This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Goodfellow, Jonathon Shlens, and Christian Szegedy

Reference 12

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This paper cites Towards lossless dataset distillation via difficulty-aligned trajectory matching.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Towards lossless dataset distillation via difficulty-aligned trajectory matching

Reference 13

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This paper cites Multisize dataset condensation.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Multisize dataset condensation

Reference 14

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This paper cites Nat- uralistic physical adversarial patch for object detectors.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Nat- uralistic physical adversarial patch for object detectors

Reference 15

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This paper cites Adver- sarial examples are not bugs, they are features.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Adver- sarial examples are not bugs, they are features

Reference 16

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Observation 9de6596b-d2bd-4ccc-a398-dc9a3a2fdf6c · outbound

This paper cites Torchattacks: A PyTorch Repository for Adversarial Attacks.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 17

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This paper cites Segment any- thing.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Segment any- thing

Reference 18

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Observation bb77f079-9062-4535-8dca-7e0db8df5e8d · outbound

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

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Learning multiple layers of features from tiny images

Reference 19

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This paper cites Imagenet classification with deep convolutional neural net- works.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Imagenet classification with deep convolutional neural net- works

Reference 20

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This paper cites Gradient-based learning applied to document recog- nition.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Gradient-based learning applied to document recog- nition

Reference 21

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This paper cites Deep learning.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Deep learning

Reference 22

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This paper cites Efficient dataset distillation using random feature ap- proximation.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Efficient dataset distillation using random feature ap- proximation

Reference 23

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This paper cites Towards Trustworthy Dataset Distillation.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Towards Trustworthy Dataset Distillation

Reference 24

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This paper cites Towards deep learn- ing models resistant to adversarial attacks.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Towards deep learn- ing models resistant to adversarial attacks

Reference 25

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

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Towards deep learning models resistant to adversarial attacks

Reference 26

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This paper cites Deepfool: a simple and accurate method to fool deep neural networks.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Deepfool: a simple and accurate method to fool deep neural networks

Reference 27

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This paper cites Dataset Meta-Learning from Kernel Ridge-Regression.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Dataset Meta-Learning from Kernel Ridge-Regression

Reference 28

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This paper cites Dataset distillation with infinitely wide convolutional networks.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Dataset distillation with infinitely wide convolutional networks

Reference 29

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This paper cites Learn- ing transferable visual models from natural language super- vision.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Learn- ing transferable visual models from natural language super- vision

Reference 30

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BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Empirical Analysis of the Hessian of Over-Parametrized Neural Networks

Reference 31

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This paper cites On the diver- sity and realism of distilled dataset: An efficient dataset dis- tillation paradigm.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation On the diver- sity and realism of distilled dataset: An efficient dataset dis- tillation paradigm

Reference 32

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BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Goodfellow, and Rob Fergus

Reference 33

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This paper cites Attention is all you need.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Attention is all you need

Reference 34

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BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Cafe: Learning to condense dataset by aligning features

Reference 35

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BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Dataset Distillation

Reference 36

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BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Stop-and-go: Exploring backdoor at- tacks on deep reinforcement learning-based traffic conges- tion control systems

Reference 37

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This paper cites Adversarial sticker: A stealthy attack method in the physical world.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Adversarial sticker: A stealthy attack method in the physical world

Reference 38

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This paper cites Simul- taneously optimizing perturbations and positions for black- box adversarial patch attacks.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Simul- taneously optimizing perturbations and positions for black- box adversarial patch attacks

Reference 39

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BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Towards Robust Dataset Learning

Reference 40

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BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation

Reference 41

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BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 42

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This paper cites Towards Adversarially Robust Dataset Distillation by Curvature Regularization.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Towards Adversarially Robust Dataset Distillation by Curvature Regularization

Reference 43

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BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective

Reference 44

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This paper cites Deep reinforcement learning-driven reconfig- urable intelligent surface-assisted radio surveillance with a fixed-wing uav.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Deep reinforcement learning-driven reconfig- urable intelligent surface-assisted radio surveillance with a fixed-wing uav

Reference 45

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BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Dataset condensation with differ- entiable siamese augmentation

Reference 46

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BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Dataset condensation with dis- tribution matching

Reference 47

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BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Dataset Condensation with Gradient Matching

Reference 48

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BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Improved distribution matching for dataset condensation

Reference 49

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This paper cites Dataset distillation using neural feature regression.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Dataset distillation using neural feature regression

Reference 50

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This paper cites Adversarial exam- ples are closely relevant to neural network models - a pre- liminary experiment explore.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Adversarial exam- ples are closely relevant to neural network models - a pre- liminary experiment explore

Reference 51

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This paper cites BACON: Bayesian Optimal Condensation Framework for Dataset Distillation.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation BACON: Bayesian Optimal Condensation Framework for Dataset Distillation

Reference 52

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 57c84262-e3b8-49d3-b10e-866bbdf96f33 · outbound

This paper cites MVPatch: More Vivid Patch for Adversarial Camouflaged Attacks on Object Detectors in the Physical World.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation MVPatch: More Vivid Patch for Adversarial Camouflaged Attacks on Object Detectors in the Physical World

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-12T20:54:53.838961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bfe3a9f8-26b6-4c9e-a3a4-ed4d0fb9f2a4 · outbound

This paper cites Additionally, we provide a configuration JSON file to facilitate the conve- nient setup and management of experimental parameters.

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Additionally, we provide a configuration JSON file to facilitate the conve- nient setup and management of experimental parameters

Reference 256

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:54:54.059175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers