34 citations · 63 across the 11 of their papers we have counts for
10 papers
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation
Zeyu Qin, Yanbo Fan, Yi Liu +4
Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples, which can produce erroneous predictions by injecting imperceptible perturbations. In this work…
Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph
Liang Li, Siwei Wang, Xinwang Liu +4
Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proved to be of…
Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods
Davoud Ataee Tarzanagh, Parvin Nazari, Bojian Hou +2
This paper introduces \textit{online bilevel optimization} in which a sequence of time-varying bilevel problems is revealed one after the other. We extend the known regret bounds f…
Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels
Chuang Zhang, Li Shen, Jian Yang +1
The memorization effect of deep neural networks (DNNs) plays a pivotal role in recent label noise learning methods. To exploit this effect, the model prediction-based methods have…
Few-Shot Adaptation of Pre-Trained Networks for Domain Shift
Wenyu Zhang, Li Shen, Wanyue Zhang +1
Deep networks are prone to performance degradation when there is a domain shift between the source (training) data and target (test) data. Recent test-time adaptation methods updat…
Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance
Shiwei Liu, Yuesong Tian, Tianlong Chen +1
Generative adversarial networks (GANs) have received an upsurging interest since being proposed due to the high quality of the generated data. While achieving increasingly impressi…