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 1471–1480 of 1746 papers

Title	Date	Tasks	Status	Hype
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning	Jun 18, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets	Jun 17, 2020	Adversarial RobustnessGeneral Classification	CodeCode Available	0
Classifier-independent Lower-Bounds for Adversarial Robustness	Jun 17, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
On sparse connectivity, adversarial robustness, and a novel model of the artificial neuron	Jun 16, 2020	Adversarial RobustnessComputational Efficiency	—Unverified	0
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness	Jun 16, 2020	Adversarial Robustness	—Unverified	0
Debona: Decoupled Boundary Network Analysis for Tighter Bounds and Faster Adversarial Robustness Proofs	Jun 16, 2020	Adversarial Robustness	CodeCode Available	0
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data	Jun 15, 2020	Adversarial RobustnessData Augmentation	—Unverified	0
On Saliency Maps and Adversarial Robustness	Jun 14, 2020	Adversarial Robustness	—Unverified	0
Achieving robustness in classification using optimal transport with hinge regularization	Jun 11, 2020	Adversarial RobustnessBinary Classification	CodeCode Available	1
Deterministic Gaussian Averaged Neural Networks	Jun 10, 2020	Adversarial Robustnessregression	CodeCode Available	0

Show:10 25 50

← PrevPage 148 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