SOTAVerified|Agents Browse Leaderboard About

Adversarial Robustness

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 911–920 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Improving Hierarchical Adversarial Robustness of Deep Neural Networks	Feb 17, 2021	Adversarial DefenseAdversarial Robustness	—Unverified	0	0
Improving Hyperspectral Adversarial Robustness Under Multiple Attacks	Oct 28, 2022	Adversarial RobustnessSemantic Segmentation	—Unverified	0	0
Improving Interpretability via Regularization of Neural Activation Sensitivity	Nov 16, 2022	Adversarial RobustnessExplanation Fidelity Evaluation	—Unverified	0	0
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks	Mar 2, 2023	Adversarial Robustness	—Unverified	0	0
Improving out-of-distribution generalization via multi-task self-supervised pretraining	Mar 30, 2020	Adversarial RobustnessDomain Generalization	—Unverified	0	0
Improving Performance of Semi-Supervised Learning by Adversarial Attacks	Aug 8, 2023	Adversarial Robustnessimage-classification	—Unverified	0	0
Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization	Nov 22, 2022	Adversarial Robustness	—Unverified	0	0
Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks	Apr 25, 2023	Adversarial RobustnessQuantization	—Unverified	0	0
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning	Jun 1, 2021	Adversarial DefenseAdversarial Robustness	—Unverified	0	0
Improving Robustness of Deep Convolutional Neural Networks via Multiresolution Learning	Sep 24, 2023	Adversarial Robustness	—Unverified	0	0

Show:10 25 50

← PrevPage 92 of 175Next →

All datasets AdvGLUE CIFAR-10 ImageNet ImageNet-A ImageNet-C Stylized ImageNet CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DeBERTa (single model)	Accuracy	0.61	—	Unverified
2	ALBERT (single model)	Accuracy	0.59	—	Unverified
3	T5 (single model)	Accuracy	0.57	—	Unverified
4	SMART_RoBERTa (single model)	Accuracy	0.54	—	Unverified
5	FreeLB (single model)	Accuracy	0.5	—	Unverified
6	RoBERTa (single model)	Accuracy	0.5	—	Unverified
7	InfoBERT (single model)	Accuracy	0.46	—	Unverified
8	ELECTRA (single model)	Accuracy	0.42	—	Unverified
9	BERT (single model)	Accuracy	0.34	—	Unverified
10	SMART_BERT (single model)	Accuracy	0.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed classifier	Accuracy	95.23	—	Unverified
2	Stochastic-LWTA/PGD/WideResNet-34-10	Accuracy	92.26	—	Unverified
3	Stochastic-LWTA/PGD/WideResNet-34-5	Accuracy	91.88	—	Unverified
4	GLOT-DR	Accuracy	84.13	—	Unverified
5	TRADES-ANCRA/ResNet18	Accuracy	81.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (SGD, Cosine)	Accuracy	77.4	—	Unverified
2	ResNet-50 (SGD, Step)	Accuracy	76.9	—	Unverified
3	DeiT-S (AdamW, Cosine)	Accuracy	76.8	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	76.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	12.2	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	3.3	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	3.2	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	3.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (AdamW, Cosine)	mean Corruption Error (mCE)	59.3	—	Unverified
2	ResNet-50 (SGD, Step)	mean Corruption Error (mCE)	57.9	—	Unverified
3	ResNet-50 (SGD, Cosine)	mean Corruption Error (mCE)	56.9	—	Unverified
4	DeiT-S (AdamW, Cosine)	mean Corruption Error (mCE)	48	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	13	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	8.4	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	8.3	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	8.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed Classifier	Clean Accuracy	85.21	—	Unverified
2	ResNet18/MART-ANCRA	Clean Accuracy	60.1	—	Unverified