106 citations · 377 across the 79 of their papers we have counts for
19 papers · 1 filter
Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT
Yulei Qin, Hao Zheng, Yun Gu +6
Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imb…
Discriminative Feature Representation with Spatio-temporal Cues for Vehicle Re-identification
J. Tu, C. Chen, X. Huang +2
Vehicle re-identification (re-ID) aims to discover and match the target vehicles from a gallery image set taken by different cameras on a wide range of road networks. It is crucial…
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines
Fanghui Liu, Xiaolin Huang, Yudong Chen +1
In this paper, we develop a quadrature framework for large-scale kernel machines via a numerical integration representation. Considering that the integration domain and measure of…
Towards Robust Neural Networks via Orthogonal Diversity
Kun Fang, Qinghua Tao, Yingwen Wu +5
Deep Neural Networks (DNNs) are vulnerable to invisible perturbations on the images generated by adversarial attacks, which raises researches on the adversarial robustness of DNNs.…
One-shot Distributed Algorithm for Generalized Eigenvalue Problem
Kexin Lv, Fan He, Xiaolin Huang +2
Nowadays, more and more datasets are stored in a distributed way for the sake of memory storage or data privacy. The generalized eigenvalue problem (GEP) plays a vital role in a la…
End-to-end Kernel Learning via Generative Random Fourier Features
Kun Fang, Fanghui Liu, Xiaolin Huang +1
Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the fi…
Relevance Attack on Detectors
Sizhe Chen, Fan He, Xiaolin Huang +1
This paper focuses on high-transferable adversarial attacks on detectors, which are hard to attack in a black-box manner, because of their multiple-output characteristics and the d…
Analysis of Regularized Least Squares in Reproducing Kernel Krein Spaces
Fanghui Liu, Lei Shi, Xiaolin Huang +2
In this paper, we study the asymptotic properties of regularized least squares with indefinite kernels in reproducing kernel Krein spaces (RKKS). By introducing a bounded hyper-sph…
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures
Fanghui Liu, Xiaolin Huang, Yingyi Chen +1
In this paper, we attempt to solve a long-lasting open question for non-positive definite (non-PD) kernels in machine learning community: can a given non-PD kernel be decomposed in…
One-shot Distibuted Algorithm for PCA with RBF Kernels
Fan He, Kexin Lv, Jie Yang +1
This letter proposes a one-shot algorithm for feature-distributed kernel PCA. Our algorithm is inspired by the dual relationship between sample-distributed and feature-distributed…
Sparse Generalized Canonical Correlation Analysis: Distributed Alternating Iteration based Approach
Jia Cai, Kexin Lv, Junyi Huo +2
Sparse canonical correlation analysis (CCA) is a useful statistical tool to detect latent information with sparse structures. However, sparse CCA works only for two datasets, i.e.,…
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond
Fanghui Liu, Xiaolin Huang, Yudong Chen +1
Random features is one of the most popular techniques to speed up kernel methods in large-scale problems. Related works have been recognized by the NeurIPS Test-of-Time award in 20…
Adversarial Imitation Attack
Mingyi Zhou, Jing Wu, Yipeng Liu +4
Deep learning models are known to be vulnerable to adversarial examples. A practical adversarial attack should require as little as possible knowledge of attacked models. Current s…
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention
Tianyi Zhang, Yun Gu, Xiaolin Huang +2
With the popularity of stereo cameras in computer assisted surgery techniques, a second viewpoint would provide additional information in surgery. However, how to effectively acces…
Double Backpropagation for Training Autoencoders against Adversarial Attack
Chengjin Sun, Sizhe Chen, Xiaolin Huang
Deep learning, as widely known, is vulnerable to adversarial samples. This paper focuses on the adversarial attack on autoencoders. Safety of the autoencoders (AEs) is important be…
Type I Attack for Generative Models
Chengjin Sun, Sizhe Chen, Jia Cai +1
Generative models are popular tools with a wide range of applications. Nevertheless, it is as vulnerable to adversarial samples as classifiers. The existing attack methods mainly f…
Real-time Image Smoothing via Iterative Least Squares
Wei Liu, Pingping Zhang, Xiaolin Huang +3
Edge-preserving image smoothing is a fundamental procedure for many computer vision and graphic applications. There is a tradeoff between the smoothing quality and the processing s…
HRFA: High-Resolution Feature-based Attack
Zhixing Ye, Sizhe Chen, Peidong Zhang +2
Adversarial attacks have long been developed for revealing the vulnerability of Deep Neural Networks (DNNs) by adding imperceptible perturbations to the input. Most methods generat…
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet
Sizhe Chen, Zhengbao He, Chengjin Sun +2
Adversarial attacks on deep neural networks (DNNs) have been found for several years. However, the existing adversarial attacks have high success rates only when the information of…