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 1291–1300 of 1746 papers

Title	Date	Tasks	Status	Hype
Bridging the Gap Between Adversarial Robustness and Optimization Bias	Feb 17, 2021	Adversarial Robustness	CodeCode Available	0
Improving Hierarchical Adversarial Robustness of Deep Neural Networks	Feb 17, 2021	Adversarial DefenseAdversarial Robustness	—Unverified	0
And/or trade-off in artificial neurons: impact on adversarial robustness	Feb 15, 2021	Adversarial RobustnessBIG-bench Machine Learning	—Unverified	0
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification	Feb 15, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Guided Interpolation for Adversarial Training	Feb 15, 2021	Adversarial Robustness	—Unverified	0
Data Quality Matters For Adversarial Training: An Empirical Study	Feb 15, 2021	Adversarial Robustness	CodeCode Available	0
Generating Structured Adversarial Attacks Using Frank-Wolfe Method	Feb 15, 2021	Adversarial Robustness	—Unverified	0
Exploring Adversarial Robustness of Deep Metric Learning	Feb 14, 2021	Adversarial RobustnessMetric Learning	CodeCode Available	0
Adversarial Robustness: What fools you makes you stronger	Feb 10, 2021	Adversarial RobustnessPAC learning	—Unverified	0
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection	Feb 10, 2021	Adversarial Robustnessfeature selection	CodeCode Available	1

Show:10 25 50

← PrevPage 130 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