9.2k citations · 16.2k across the 175 of their papers we have counts for
10 papers · 1 filter
Towards Instance-adaptive Inference for Federated Learning
Chun-Mei Feng, Kai Yu, Nian Liu +3
Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. However, the performance of…
Improving Transferability of Adversarial Examples via Bayesian Attacks
Qizhang Li, Yiwen Guo, Xiaochen Yang +2
The transferability of adversarial examples allows for the attack on unknown deep neural networks (DNNs), posing a serious threat to many applications and attracting great attentio…
Improving Adversarial Transferability via Intermediate-level Perturbation Decay
Qizhang Li, Yiwen Guo, Wangmeng Zuo +1
Intermediate-level attacks that attempt to perturb feature representations following an adversarial direction drastically have shown favorable performance in crafting transferable…
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples
Qizhang Li, Yiwen Guo, Wangmeng Zuo +1
The transferability of adversarial examples across deep neural networks (DNNs) is the crux of many black-box attacks. Many prior efforts have been devoted to improving the transfer…
Squeeze Training for Adversarial Robustness
Qizhang Li, Yiwen Guo, Wangmeng Zuo +1
The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted great attention in the machine learning community. The problem is related to non-flatness and…
Model Inconsistent but Correlated Noise: Multi-view Subspace Learning with Regularized Mixture of Gaussians
Hongwei Yong, Deyu Meng, Jinxing Li +2
Multi-view subspace learning (MSL) aims to find a low-dimensional subspace of the data obtained from multiple views. Different from single view case, MSL should take both common an…
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
Ruijia Xu, Ziliang Chen, Wangmeng Zuo +2
Unsupervised domain adaptation (UDA) conventionally assumes labeled source samples coming from a single underlying source distribution. Whereas in practical scenario, labeled data…
F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation
Xiaohe Wu, Wangmeng Zuo, Yuanyuan Zhu +1
The generalization error bound of support vector machine (SVM) depends on the ratio of radius and margin, while standard SVM only considers the maximization of the margin but ignor…
Iterated Support Vector Machines for Distance Metric Learning
Wangmeng Zuo, Faqiang Wang, David Zhang +4
Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for cl…
A Kernel Classification Framework for Metric Learning
Faqiang Wang, Wangmeng Zuo, Lei Zhang +2
Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in…