67 citations · 198 across the 24 of their papers we have counts for
24 papers
Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs
Jiani Liu, Qinghua Tao, Ce Zhu +3
Multitask learning (MTL) leverages task-relatedness to enhance performance. With the emergence of multimodal data, tasks can now be referenced by multiple indices. In this paper, w…
Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective
Zhengbao He, Tao Li, Sizhe Chen +1
Although fast adversarial training provides an efficient approach for building robust networks, it may suffer from a serious problem known as catastrophic overfitting (CO), where m…
A Decentralized Framework for Kernel PCA with Projection Consensus Constraints
Fan He, Ruikai Yang, Lei Shi +1
This paper studies kernel PCA in a decentralized setting, where data are distributively observed with full features in local nodes and a fusion center is prohibited. Compared with…
Efficient Generalization Improvement Guided by Random Weight Perturbation
Tao Li, Weihao Yan, Zehao Lei +4
To fully uncover the great potential of deep neural networks (DNNs), various learning algorithms have been developed to improve the model's generalization ability. Recently, sharpn…
Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors
Sizhe Chen, Geng Yuan, Xinwen Cheng +4
As data becomes increasingly vital, a company would be very cautious about releasing data, because the competitors could use it to train high-performance models, thereby posing a t…
Multi-head Ensemble of Smoothed Classifiers for Certified Robustness
Kun Fang, Qinghua Tao, Yingwen Wu +3
Randomized Smoothing (RS) is a promising technique for certified robustness, and recently in RS the ensemble of multiple Deep Neural Networks (DNNs) has shown state-of-the-art perf…
FG-UAP: Feature-Gathering Universal Adversarial Perturbation
Zhixing Ye, Xinwen Cheng, Xiaolin Huang
Deep Neural Networks (DNNs) are susceptible to elaborately designed perturbations, whether such perturbations are dependent or independent of images. The latter one, called Univers…
Unifying Gradients to Improve Real-world Robustness for Deep Networks
Yingwen Wu, Sizhe Chen, Kun Fang +1
The wide application of deep neural networks (DNNs) demands an increasing amount of attention to their real-world robustness, i.e., whether a DNN resists black-box adversarial atta…
One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks
Shutong Wu, Sizhe Chen, Cihang Xie +1
Unlearnable examples (ULEs) aim to protect data from unauthorized usage for training DNNs. Existing work adds -bounded perturbations to the original sample so that the…
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks
Sizhe Chen, Zhehao Huang, Qinghua Tao +3
The score-based query attacks (SQAs) pose practical threats to deep neural networks by crafting adversarial perturbations within dozens of queries, only using the model's output sc…
Query Attack by Multi-Identity Surrogates
Sizhe Chen, Zhehao Huang, Qinghua Tao +1
Deep Neural Networks (DNNs) are acknowledged as vulnerable to adversarial attacks, while the existing black-box attacks require extensive queries on the victim DNN to achieve high…
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching
Fanghui Liu, Chen Gong, Xiaolin Huang +3
In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs)…
Fast Signal Recovery from Saturated Measurements by Linear Loss and Nonconvex Penalties
Fan He, Xiaolin Huang, Yipeng Liu +1
Sign information is the key to overcoming the inevitable saturation error in compressive sensing systems, which causes information loss and results in bias. For sparse signal recov…
Adversarial Attack Type I: Cheat Classifiers by Significant Changes
Sanli Tang, Xiaolin Huang, Mingjian Chen +2
Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. In this paper, we propose another type of a…
Learning Data-adaptive Nonparametric Kernels
Fanghui Liu, Xiaolin Huang, Chen Gong +2
In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adap…
Scale-Space Anisotropic Total Variation for Limited Angle Tomography
Yixing Huang, Oliver Taubmann, Xiaolin Huang +3
This paper addresses streak reduction in limited angle tomography. Although the iterative reweighted total variation (wTV) algorithm reduces small streaks well, it is rather inept…
Edge-Preserving Piecewise Linear Image Smoothing Using Piecewise Constant Filters
Wei Liu, Wei Xu, Xiaogang Chen +3
Most image smoothing filters in the literature assume a piecewise constant model of smoothed output images. However, the piecewise constant model assumption can cause artifacts suc…
Indefinite Kernel Logistic Regression with Concave-inexact-convex Procedure
Fanghui Liu, Xiaolin Huang, Chen Gong +2
In kernel methods, the kernels are often required to be positive definite, which restricts the use of many indefinite kernels. To consider those non-positive definite kernels, in t…
Nonconvex penalties with analytical solutions for one-bit compressive sensing
Xiaolin Huang, Ming Yan
One-bit measurements widely exist in the real world, and they can be used to recover sparse signals. This task is known as the problem of learning halfspaces in learning theory and…
Online Robust Principal Component Analysis with Change Point Detection
Wei Xiao, Xiaolin Huang, Jorge Silva +2
Robust PCA methods are typically batch algorithms which requires loading all observations into memory before processing. This makes them inefficient to process big data. In this pa…
Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction
Xiaolin Huang, Yan Xia, Lei Shi +4
When a measurement falls outside the quantization or measurable range, it becomes saturated and cannot be used in classical reconstruction methods. For example, in C-arm angiograph…
Adaptive Block Coordinate DIRECT algorithm
Qinghua Tao, Xiaolin Huang, Shuning Wang +1
DIviding RECTangles (DIRECT) is an efficient and popular method in dealing with bound constrained optimization problems. However, DIRECT suffers from dimension curse, since its com…
Pinball Loss Minimization for One-bit Compressive Sensing: Convex Models and Algorithms
Xiaolin Huang, Lei Shi, Ming Yan +1
The one-bit quantization is implemented by one single comparator that operates at low power and a high rate. Hence one-bit compressive sensing (1bit-CS) becomes attractive in signa…
Nonconvex Sorted Minimization for Sparse Approximation
Xiaolin Huang, Lei Shi, Ming Yan
The norm is the tight convex relaxation for the "norm" and has been successfully applied for recovering sparse signals. For problems with fewer samplings, one nee…