Search Results for author: Xiaolin Huang

Found 84 papers, 36 papers with code

Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning

1 code implementation3 Jun 2024 Fan He, Mingzhen He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens

Ridgeless regression has garnered attention among researchers, particularly in light of the ``Benign Overfitting'' phenomenon, where models interpolating noisy samples demonstrate robust generalization.

regression

Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection

no code implementations28 May 2024 Yingwen Wu, Ruiji Yu, Xinwen Cheng, Zhengbao He, Xiaolin Huang

In the open world, detecting out-of-distribution (OOD) data, whose labels are disjoint with those of in-distribution (ID) samples, is important for reliable deep neural networks (DNNs).

Data Augmentation Out-of-Distribution Detection

Towards Natural Machine Unlearning

no code implementations24 May 2024 Zhengbao He, Tao Li, Xinwen Cheng, Zhehao Huang, Xiaolin Huang

Towards more \textit{natural} machine unlearning, we inject correct information from the remaining data to the forgetting samples when changing their labels.

Machine Unlearning

Decentralized Kernel Ridge Regression Based on Data-dependent Random Feature

1 code implementation13 May 2024 Ruikai Yang, Fan He, Mingzhen He, Jie Yang, Xiaolin Huang

Random feature (RF) has been widely used for node consistency in decentralized kernel ridge regression (KRR).

regression

Data Imputation by Pursuing Better Classification: A Supervised Kernel-Based Method

1 code implementation13 May 2024 Ruikai Yang, Fan He, Mingzhen He, Kaijie Wang, Xiaolin Huang

In this paper, we propose a new framework that effectively leverages supervision information to complete missing data in a manner conducive to classification.

Imputation

Resolve Domain Conflicts for Generalizable Remote Physiological Measurement

1 code implementation11 Apr 2024 Weiyu Sun, Xinyu Zhang, Hao Lu, Ying Chen, Yun Ge, Xiaolin Huang, Jie Yuan, Yingcong Chen

Remote photoplethysmography (rPPG) technology has become increasingly popular due to its non-invasive monitoring of various physiological indicators, making it widely applicable in multimedia interaction, healthcare, and emotion analysis.

Attribute Emotion Recognition +1

Revisiting Random Weight Perturbation for Efficiently Improving Generalization

1 code implementation30 Mar 2024 Tao Li, Qinghua Tao, Weihao Yan, Zehao Lei, Yingwen Wu, Kun Fang, Mingzhen He, Xiaolin Huang

Improving the generalization ability of modern deep neural networks (DNNs) is a fundamental challenge in machine learning.

OrthCaps: An Orthogonal CapsNet with Sparse Attention Routing and Pruning

no code implementations CVPR 2024 Xinyu Geng, JiaMing Wang, Jiawei Gong, Yuerong Xue, Jun Xu, Fanglin Chen, Xiaolin Huang

Redundancy is a persistent challenge in Capsule Networks (CapsNet), leading to high computational costs and parameter counts.

Friendly Sharpness-Aware Minimization

1 code implementation CVPR 2024 Tao Li, Pan Zhou, Zhengbao He, Xinwen Cheng, Xiaolin Huang

By decomposing the adversarial perturbation in SAM into full gradient and stochastic gradient noise components, we discover that relying solely on the full gradient component degrades generalization while excluding it leads to improved performance.

Inverse-Free Fast Natural Gradient Descent Method for Deep Learning

no code implementations6 Mar 2024 Xinwei Ou, Ce Zhu, Xiaolin Huang, Yipeng Liu

Second-order optimization techniques have the potential to achieve faster convergence rates compared to first-order methods through the incorporation of second-order derivatives or statistics.

Image Classification Machine Translation +1

Machine Unlearning by Suppressing Sample Contribution

no code implementations23 Feb 2024 Xinwen Cheng, Zhehao Huang, Xiaolin Huang

Machine Unlearning (MU) is to forget data from a well-trained model, which is practically important due to the "right to be forgotten".

Machine Unlearning

Learn What You Need in Personalized Federated Learning

1 code implementation16 Jan 2024 Kexin Lv, Rui Ye, Xiaolin Huang, Jie Yang, Siheng Chen

Personalized federated learning aims to address data heterogeneity across local clients in federated learning.

Image Classification Personalized Federated Learning

Online Continual Learning via Logit Adjusted Softmax

1 code implementation11 Nov 2023 Zhehao Huang, Tao Li, Chenhe Yuan, Yingwen Wu, Xiaolin Huang

Online continual learning is a challenging problem where models must learn from a non-stationary data stream while avoiding catastrophic forgetting.

Continual Learning

Self-similarity Prior Distillation for Unsupervised Remote Physiological Measurement

no code implementations9 Nov 2023 Xinyu Zhang, Weiyu Sun, Hao Lu, Ying Chen, Yun Ge, Xiaolin Huang, Jie Yuan, Yingcong Chen

In this paper, we propose a Self-Similarity Prior Distillation (SSPD) framework for unsupervised rPPG estimation, which capitalizes on the intrinsic self-similarity of cardiac activities.

Contrastive Learning

Low-Dimensional Gradient Helps Out-of-Distribution Detection

no code implementations26 Oct 2023 Yingwen Wu, Tao Li, Xinwen Cheng, Jie Yang, Xiaolin Huang

To bridge this gap, in this paper, we conduct a comprehensive investigation into leveraging the entirety of gradient information for OOD detection.

Dimensionality Reduction Out-of-Distribution Detection

Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape Perspective

1 code implementation22 Oct 2023 Kun Fang, Qinghua Tao, Xiaolin Huang, Jie Yang

Motivated by such diversities on OoD loss landscape across modes, we revisit the deep ensemble method for OoD detection through mode ensemble, leading to improved performance and benefiting the OoD detector with reduced variances.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels

1 code implementation8 Oct 2023 Fan He, Mingzhen He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens

To enhance kernel flexibility, this paper introduces the concept of Locally-Adaptive-Bandwidths (LAB) as trainable parameters to enhance the Radial Basis Function (RBF) kernel, giving rise to the LAB RBF kernel.

regression

Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs

1 code implementation30 Aug 2023 Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Xiaolin Huang, Johan A. K. Suykens

In contrast to previous MTL frameworks, our decision function in the dual induces a weighted kernel function with a task-coupling term characterized by the similarities of the task-specific factors, better revealing the explicit relations across tasks in MTL.

Data-Driven Safe Controller Synthesis for Deterministic Systems: A Posteriori Method With Validation Tests

no code implementations3 Apr 2023 Yu Chen, Chao Shang, Xiaolin Huang, Xiang Yin

We first formulate the safety synthesis problem as a robust convex program (RCP) based on notion of control barrier function.

PCR-CG: Point Cloud Registration via Deep Explicit Color and Geometry

1 code implementation28 Feb 2023 Yu Zhang, Junle Yu, Xiaolin Huang, Wenhui Zhou, Ji Hou

Different from previous methods that only use geometry representation, our module is specifically designed to effectively correlate color into geometry for the point cloud registration task.

Point Cloud Registration

Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective

no code implementations23 Feb 2023 Zhengbao He, Tao Li, Sizhe Chen, Xiaolin Huang

Based on self-fitting, we provide new insights into the existing methods to mitigate CO and extend CO to multi-step adversarial training.

Self-Learning

Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors

1 code implementation22 Nov 2022 Sizhe Chen, Geng Yuan, Xinwen Cheng, Yifan Gong, Minghai Qin, Yanzhi Wang, Xiaolin Huang

In this paper, we uncover them by model checkpoints' gradients, forming the proposed self-ensemble protection (SEP), which is very effective because (1) learning on examples ignored during normal training tends to yield DNNs ignoring normal examples; (2) checkpoints' cross-model gradients are close to orthogonal, meaning that they are as diverse as DNNs with different architectures.

Efficient Generalization Improvement Guided by Random Weight Perturbation

1 code implementation21 Nov 2022 Tao Li, Weihao Yan, Zehao Lei, Yingwen Wu, Kun Fang, Ming Yang, Xiaolin Huang

To fully uncover the great potential of deep neural networks (DNNs), various learning algorithms have been developed to improve the model's generalization ability.

On Multi-head Ensemble of Smoothed Classifiers for Certified Robustness

1 code implementation20 Nov 2022 Kun Fang, Qinghua Tao, Yingwen Wu, Tao Li, Xiaolin Huang, Jie Yang

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 performances.

FG-UAP: Feature-Gathering Universal Adversarial Perturbation

no code implementations27 Sep 2022 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.

Random Fourier Features for Asymmetric Kernels

no code implementations18 Sep 2022 Mingzhen He, Fan He, Fanghui Liu, Xiaolin Huang

The theoretical foundation of RFFs is based on the Bochner theorem that relates symmetric, positive definite (PD) functions to probability measures.

Computational Efficiency

Unifying Gradients to Improve Real-world Robustness for Deep Networks

1 code implementation12 Aug 2022 Yingwen Wu, Sizhe Chen, Kun Fang, Xiaolin Huang

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 attacks, among which score-based query attacks (SQAs) are most threatening since they can effectively hurt a victim network with the only access to model outputs.

BYHE: A Simple Framework for Boosting End-to-end Video-based Heart Rate Measurement Network

no code implementations4 Jul 2022 Weiyu Sun, Xinyu Zhang, Ying Chen, Yun Ge, Chunyu Ji, Xiaolin Huang

Heart rate measuring based on remote photoplethysmography (rPPG) plays an important role in health caring, which estimates heart rate from facial video in a non-contact, less-constrained way.

Heart rate estimation

Piecewise Linear Neural Networks and Deep Learning

no code implementations18 Jun 2022 Qinghua Tao, Li Li, Xiaolin Huang, Xiangming Xi, Shuning Wang, Johan A. K. Suykens

To apply PWLNN methods, both the representation and the learning have long been studied.

Trainable Weight Averaging: A General Approach for Subspace Training

1 code implementation26 May 2022 Tao Li, Zhehao Huang, Yingwen Wu, Zhengbao He, Qinghua Tao, Xiaolin Huang, Chih-Jen Lin

Training deep neural networks (DNNs) in low-dimensional subspaces is a promising direction for achieving efficient training and better generalization performance.

Dimensionality Reduction Efficient Neural Network +3

One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks

1 code implementation24 May 2022 Shutong Wu, Sizhe Chen, Cihang Xie, Xiaolin Huang

Based on OPS, we introduce an unlearnable dataset called CIFAR-10-S, which is indistinguishable from CIFAR-10 by humans but induces the trained model to extremely low accuracy.

Data Augmentation

Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks

1 code implementation24 May 2022 Sizhe Chen, Zhehao Huang, Qinghua Tao, Yingwen Wu, Cihang Xie, Xiaolin Huang

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 scores.

Adversarial Attack

Learning Perspective Deformation in X-Ray Transmission Imaging

no code implementations13 Feb 2022 Yixing Huang, Andreas Maier, Fuxin Fan, Björn Kreher, Xiaolin Huang, Rainer Fietkau, Christoph Bert, Florian Putz

The complementary view setting provides a practical way to identify perspectively deformed structures by assessing the deviation between the two views.

Learning with Asymmetric Kernels: Least Squares and Feature Interpretation

no code implementations3 Feb 2022 Mingzhen He, Fan He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens

Asymmetric kernels naturally exist in real life, e. g., for conditional probability and directed graphs.

Subspace Adversarial Training

1 code implementation CVPR 2022 Tao Li, Yingwen Wu, Sizhe Chen, Kun Fang, Xiaolin Huang

Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust.

Semi-tensor Product-based TensorDecomposition for Neural Network Compression

no code implementations30 Sep 2021 Hengling Zhao, Yipeng Liu, Xiaolin Huang, Ce Zhu

Tucker decomposition, Tensor Train (TT) and Tensor Ring (TR) are common decomposition for low rank compression of deep neural networks.

Low-rank compression Neural Network Compression +1

A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing

1 code implementation15 Jul 2021 Wei Liu, Pingping Zhang, Yinjie Lei, Xiaolin Huang, Jie Yang, Michael Ng

The effectiveness and superior performance of our approach are validated through comprehensive experiments in a range of applications.

image smoothing

Adaptive Feature Alignment for Adversarial Training

no code implementations31 May 2021 Tao Wang, Ruixin Zhang, Xingyu Chen, Kai Zhao, Xiaolin Huang, Yuge Huang, Shaoxin Li, Jilin Li, Feiyue Huang

Based on this observation, we propose the adaptive feature alignment (AFA) to generate features of arbitrary attacking strengths.

Adversarial Defense

Dominant Patterns: Critical Features Hidden in Deep Neural Networks

no code implementations31 May 2021 Zhixing Ye, Shaofei Qin, Sizhe Chen, Xiaolin Huang

As the name suggests, for a natural image, if we add the dominant pattern of a DNN to it, the output of this DNN is determined by the dominant pattern instead of the original image, i. e., DNN's prediction is the same with the dominant pattern's.

Query Attack by Multi-Identity Surrogates

2 code implementations31 May 2021 Sizhe Chen, Zhehao Huang, Qinghua Tao, Xiaolin Huang

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 success rates.

Residual Enhanced Multi-Hypergraph Neural Network

1 code implementation2 May 2021 Jing Huang, Xiaolin Huang, Jie Yang

Hypergraphs are a generalized data structure of graphs to model higher-order correlations among entities, which have been successfully adopted into various research domains.

Representation Learning

Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernels

1 code implementation13 Apr 2021 Qin Luo, Kun Fang, Jie Yang, Xiaolin Huang

Random Fourier Features (RFF) demonstrate wellappreciated performance in kernel approximation for largescale situations but restrict kernels to be stationary and positive definite.

regression

Weighted Neural Tangent Kernel: A Generalized and Improved Network-Induced Kernel

1 code implementation22 Mar 2021 Lei Tan, Shutong Wu, Xiaolin Huang

In this paper, we introduce the Weighted Neural Tangent Kernel (WNTK), a generalized and improved tool, which can capture an over-parameterized NN's training dynamics under different optimizers.

regression

Low Dimensional Landscape Hypothesis is True: DNNs can be Trained in Tiny Subspaces

1 code implementation20 Mar 2021 Tao Li, Lei Tan, Qinghua Tao, Yipeng Liu, Xiaolin Huang

Deep neural networks (DNNs) usually contain massive parameters, but there is redundancy such that it is guessed that the DNNs could be trained in low-dimensional subspaces.

Dimensionality Reduction

Measuring the Transferability of $\ell_\infty$ Attacks by the $\ell_2$ Norm

no code implementations20 Feb 2021 Sizhe Chen, Qinghua Tao, Zhixing Ye, Xiaolin Huang

Deep neural networks could be fooled by adversarial examples with trivial differences to original samples.

Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT

1 code implementation10 Dec 2020 Yulei Qin, Hao Zheng, Yun Gu, Xiaolin Huang, Jie Yang, Lihui Wang, Feng Yao, Yue-Min Zhu, Guang-Zhong Yang

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 imbalance between tubular targets and background.

Anatomy Representation Learning +1

Towards a Unified Quadrature Framework for Large-Scale Kernel Machines

no code implementations3 Nov 2020 Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens

In this paper, we develop a quadrature framework for large-scale kernel machines via a numerical integration representation.

Numerical Integration

Towards Robust Neural Networks via Orthogonal Diversity

2 code implementations23 Oct 2020 Kun Fang, Qinghua Tao, Yingwen Wu, Tao Li, Jia Cai, Feipeng Cai, Xiaolin Huang, Jie Yang

In this way, the proposed DIO augments the model and enhances the robustness of DNN itself as the learned features can be corrected by these mutually-orthogonal paths.

Adversarial Robustness Data Augmentation

One-shot Distributed Algorithm for Generalized Eigenvalue Problem

no code implementations22 Oct 2020 Kexin Lv, Fan He, Xiaolin Huang, Jie Yang, Liming Chen

Nowadays, more and more datasets are stored in a distributed way for the sake of memory storage or data privacy.

Learn Robust Features via Orthogonal Multi-Path

no code implementations28 Sep 2020 Kun Fang, Xiaolin Huang, Yingwen Wu, Tao Li, Jie Yang

To defend adversarial attacks, we design a block containing multiple paths to learn robust features and the parameters of these paths are required to be orthogonal with each other.

End-to-end Kernel Learning via Generative Random Fourier Features

1 code implementation10 Sep 2020 Kun Fang, Fanghui Liu, Xiaolin Huang, Jie Yang

In the second-stage process, a linear learner is conducted with respect to the mapped random features.

Adversarial Robustness

Relevance Attack on Detectors

1 code implementation16 Aug 2020 Sizhe Chen, Fan He, Xiaolin Huang, Kun Zhang

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 diversity across architectures.

Autonomous Driving Instance Segmentation +4

Analysis of Regularized Least Squares in Reproducing Kernel Krein Spaces

no code implementations1 Jun 2020 Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens

In this paper, we study the asymptotic properties of regularized least squares with indefinite kernels in reproducing kernel Krein spaces (RKKS).

Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures

no code implementations30 May 2020 Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens

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 into the difference of two PD kernels (termed as positive decomposition)?

Open-Ended Question Answering

One-shot Distibuted Algorithm for PCA with RBF Kernels

1 code implementation6 May 2020 Fan He, Kexin Lv, Jie Yang, Xiaolin Huang

This letter proposes a one-shot algorithm for feature-distributed kernel PCA.

Sparse Generalized Canonical Correlation Analysis: Distributed Alternating Iteration based Approach

no code implementations23 Apr 2020 Jia Cai, Kexin Lv, Junyi Huo, Xiaolin Huang, Jie Yang

To overcome this limitation, in this paper, we propose a sparse generalized canonical correlation analysis (GCCA), which could detect the latent relations of multiview data with sparse structures.

Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond

no code implementations23 Apr 2020 Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens

This survey may serve as a gentle introduction to this topic, and as a users' guide for practitioners interested in applying the representative algorithms and understanding theoretical results under various technical assumptions.

Adversarial Imitation Attack

no code implementations28 Mar 2020 Mingyi Zhou, Jing Wu, Yipeng Liu, Xiaolin Huang, Shuaicheng Liu, Xiang Zhang, Ce Zhu

Then, the adversarial examples generated by the imitation model are utilized to fool the attacked model.

Adversarial Attack

Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention

no code implementations19 Mar 2020 Tianyi Zhang, Yun Gu, Xiaolin Huang, Enmei Tu, Jie Yang

In particular, we incorporate a disparity-based constraint mechanism into the generation of SR images in a deep neural network framework with an additional atrous parallax-attention modules.

Image Super-Resolution

Type I Attack for Generative Models

no code implementations4 Mar 2020 Chengjin Sun, Sizhe Chen, Jia Cai, Xiaolin Huang

To implement the Type I attack, we destroy the original one by increasing the distance in input space while keeping the output similar because different inputs may correspond to similar features for the property of deep neural network.

Vocal Bursts Type Prediction

Double Backpropagation for Training Autoencoders against Adversarial Attack

no code implementations4 Mar 2020 Chengjin Sun, Sizhe Chen, Xiaolin Huang

We restrict the gradient from the reconstruction image to the original one so that the autoencoder is not sensitive to trivial perturbation produced by the adversarial attack.

Adversarial Attack Robust classification

HRFA: High-Resolution Feature-based Attack

no code implementations21 Jan 2020 Zhixing Ye, Sizhe Chen, Peidong Zhang, Chengjin Sun, Xiaolin Huang

Adversarial attacks have long been developed for revealing the vulnerability of Deep Neural Networks (DNNs) by adding imperceptible perturbations to the input.

Denoising Face Verification +1

Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet

no code implementations16 Jan 2020 Sizhe Chen, Zhengbao He, Chengjin Sun, Jie Yang, Xiaolin Huang

AoA enjoys a significant increase in transferability when the traditional cross entropy loss is replaced with the attention loss.

Adversarial Attack

Mixed-Precision Quantized Neural Network with Progressively Decreasing Bitwidth For Image Classification and Object Detection

no code implementations29 Dec 2019 Tianshu Chu, Qin Luo, Jie Yang, Xiaolin Huang

In addition, the results also demonstrate that the higher-precision bottom layers could boost the 1-bit network performance appreciably due to a better preservation of the original image information while the lower-precision posterior layers contribute to the regularization of $k-$bit networks.

General Classification Image Classification +3

DAmageNet: A Universal Adversarial Dataset

1 code implementation16 Dec 2019 Sizhe Chen, Xiaolin Huang, Zhengbao He, Chengjin Sun

Adversarial samples are similar to the clean ones, but are able to cheat the attacked DNN to produce incorrect predictions in high confidence.

Adversarial Attack

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

no code implementations20 Nov 2019 Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan A. K. Suykens

In this paper, we propose a fast surrogate leverage weighted sampling strategy to generate refined random Fourier features for kernel approximation.

Deep Kernel Learning via Random Fourier Features

no code implementations7 Oct 2019 Jiaxuan Xie, Fanghui Liu, Kaijie Wang, Xiaolin Huang

On small datasets (less than 1000 samples), for which deep learning is generally not suitable due to overfitting, our method achieves superior performance compared to advanced kernel methods.

Small Data Image Classification

Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior

no code implementations19 Aug 2019 Yixing Huang, Alexander Preuhs, Guenter Lauritsch, Michael Manhart, Xiaolin Huang, Andreas Maier

Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data; b) deep learning methods are sensitive to noise.

A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing

1 code implementation23 Jul 2019 Wei Liu, Pingping Zhang, Yinjie Lei, Xiaolin Huang, Jie Yang, Ian Reid

In this paper, a non-convex non-smooth optimization framework is proposed to achieve diverse smoothing natures where even contradictive smoothing behaviors can be achieved.

image smoothing

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

no code implementations15 Apr 2019 Fanghui Liu, Chen Gong, Xiaolin Huang, Tao Zhou, Jie Yang, DaCheng Tao

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).

Template Matching Visual Tracking

Online PCB Defect Detector On A New PCB Defect Dataset

1 code implementation17 Feb 2019 Sanli Tang, Fan He, Xiaolin Huang, Jie Yang

To train the deep model, a dataset is established, namely DeepPCB, which contains 1, 500 image pairs with annotations including positions of 6 common types of PCB defects.

Defect Detection

Varifocal-Net: A Chromosome Classification Approach using Deep Convolutional Networks

1 code implementation13 Oct 2018 Yulei Qin, Juan Wen, Hao Zheng, Xiaolin Huang, Jie Yang, Ning Song, Yue-Min Zhu, Lingqian Wu, Guang-Zhong Yang

To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosome's type and polarity using deep convolutional networks.

Classification General Classification +2

Generalization Properties of hyper-RKHS and its Applications

no code implementations26 Sep 2018 Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens

This paper generalizes regularized regression problems in a hyper-reproducing kernel Hilbert space (hyper-RKHS), illustrates its utility for kernel learning and out-of-sample extensions, and proves asymptotic convergence results for the introduced regression models in an approximation theory view.

Learning Theory regression

Adversarial Attack Type I: Cheat Classifiers by Significant Changes

no code implementations3 Sep 2018 Sanli Tang, Xiaolin Huang, Mingjian Chen, Chengjin Sun, Jie Yang

Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations.

Adversarial Attack Vocal Bursts Type Prediction

Learning Data-adaptive Nonparametric Kernels

no code implementations31 Aug 2018 Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li

Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility.

Indefinite Kernel Logistic Regression with Concave-inexact-convex Procedure

no code implementations6 Jul 2017 Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Johan A. K. Suykens

Since the concave-convex procedure has to solve a sub-problem in each iteration, we propose a concave-inexact-convex procedure (CCICP) algorithm with an inexact solving scheme to accelerate the solving process.

regression

Nonconvex penalties with analytical solutions for one-bit compressive sensing

no code implementations4 Jun 2017 Xiaolin Huang, Ming Yan

For several nonconvex penalties, including minimax concave penalty (MCP), $\ell_0$ norm, and sorted $\ell_1$ penalty, we provide fast algorithms for finding the analytical solutions by solving the dual problem.

Compressive Sensing Learning Theory

Online Robust Principal Component Analysis with Change Point Detection

2 code implementations19 Feb 2017 Wei Xiao, Xiaolin Huang, Jorge Silva, Saba Emrani, Arin Chaudhuri

Robust PCA methods are typically batch algorithms which requires loading all observations into memory before processing.

Change Point Detection Two-sample testing

Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction

no code implementations3 Jan 2017 Xiaolin Huang, Yan Xia, Lei Shi, Yixing Huang, Ming Yan, Joachim Hornegger, Andreas Maier

Aiming at overexposure correction for computed tomography (CT) reconstruction, we in this paper propose a mixed one-bit compressive sensing (M1bit-CS) to acquire information from both regular and saturated measurements.

Compressive Sensing Computed Tomography (CT) +1

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