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arXiv 2024年

作者： Zhang, Xu Jia, Yuheng Song, Mofei Wang, Ran School of Computer Science and Engineering South University Nanjing210093 China School of Mathematical Sciences Shenzhen University Shenzhen518060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

Ensemble clustering aggregates multiple weak clusterings to achieve a more accurate and robust consensus result. The Co-Association matrix (CA matrix) based method is the mainstream ensemble clustering approach that constructs the similarity relationships between sample pairs according the weak clustering partitions to generate the final clustering result. However, the existing methods neglect that the quality of cluster is related to its size, i.e., a cluster with smaller size tends to higher accuracy. Moreover, they also do not consider the valuable dissimilarity information in the base clusterings which can reflect the varying importance of sample pairs that are completely disconnected. To this end, we propose the Similarity and Dissimilarity Guided Co-association matrix (SDGCA) to achieve ensemble clustering. First, we introduce normalized ensemble entropy to estimate the quality of each cluster, and construct a similarity matrix based on this estimation. Then, we employ the random walk to explore high-order proximity of base clusterings to construct a dissimilarity matrix. Finally, the adversarial relationship between the similarity matrix and the dissimilarity matrix is utilized to construct a promoted CA matrix for ensemble clustering. We compared our method with 13 state-of-the-art methods across 12 datasets, and the results demonstrated the superiority clustering ability and robustness of the proposed approach. The code is available at https://***/xuz2019/SDGCA. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

MMA regularization: decorrelating weights of neural networks by maximizing the minimal angles 20

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MMA regularization: decorrelating weights of neural networks...

Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Zhennan Wang Canqun Xiang Wenbin Zou Chen Xu Shenzhen Key Laboratory of Advanced Machine Learning and Applications Guangdong Key Laboratory of Intelligent Information Processing Institute of Artificial Intelligence and Advanced Communication College of Electronics and Information Engineering Shenzhen University Institute of Artificial Intelligence and Advanced Communication College of Mathematics and Statistics Shenzhen University

ISBN: (纸本)9781713829546

The strong correlation between neurons or filters can significantly weaken the generalization ability of neural networks. Inspired by the well-known Tammes problem, we propose a novel diversity regularization method to address this issue, which makes the normalized weight vectors of neurons or filters distributed on a hypersphere as uniformly as possible, through maximizing the minimal pairwise angles (MMA). This method can easily exert its effect by plugging the MMA regularization term into the loss function with negligible computational overhead. The MMA regularization is simple, efficient, and effective. Therefore, it can be used as a basic regularization method in neural network training. Extensive experiments demonstrate that MMA regularization is able to enhance the generalization ability of various modern models and achieves considerable performance improvements on CIFAR100 and TinyImageNet datasets. In addition, experiments on face verification show that MMA regularization is also effective for feature learning.

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Orthogonal subspace based fast iterative thresholding algorithms for joint sparsity recovery

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arXiv 2021年

作者： Han, Ningning Li, Shidong Lu, Jian Shenzhen Key Laboratory of Advanced Machine Learning and Applications College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Department of Mathematics San Francisco State University San FranciscoCA94132 United States

Sparse signal recoveries from multiple measurement vectors (MMV) with joint sparsity property have many applications in signal, image, and video processing. The problem becomes much more involved when snapshots of the signal matrix are temporally correlated. With signal’s temporal correlation in mind, we provide a framework of iterative MMV algorithms based on thresholding, functional feedback and null space tuning. Convergence analysis for exact recovery is established. Unlike most of iterative greedy algorithms that select indices in a measurement/solution space, we determine indices based on an orthogonal subspace spanned by the iterative sequence. In addition, a functional feedback that controls the amount of energy relocation from the "tails" is implemented and analyzed. It is seen that the principle of functional feedback is capable to lower the number of iteration and speed up the convergence of the algorithm. Numerical experiments demonstrate that the proposed algorithm has a clearly advantageous balance of efficiency, adaptivity and accuracy compared with other state-of-the-art algorithms. © 2021, CC BY.

关键词： Vectors

HOCA: Higher-Order Channel Attention for Single Image Super-Resolution

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HOCA: Higher-Order Channel Attention for Single Image Super-...

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yalei Lv Tao Dai Bin Chen Jian Lu Shu-Tao Xia Jingchao Cao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University China City University of Hong Kong Hong Kong

Convolutional neural networks (CNNs) have obtained great success in single image super-resolution (SR). More recent works (e.g., RCAN and SAN) have obtained remarkable performance with channel attention based on first- or second-order statistics of features. However, these methods neglect the rich feature statistics higher than second-order, thus hindering the representation ability of CNNs. To address this issue, we propose a higher-order channel attention (HOCA) module to enhance the representation ability of CNNs. In our HOCA module, to capture different types of semantic information, we first compute k-order of feature statistics, followed by channel attention to learn the feature interdependencies. Considering the diversity of input contents, we design a gate mechanism to adaptively select a specific k-order channel attention. Besides, our HOCA module serves as a plug-and-play module and can be easily plugged into existing state-of-art CNN-based SR methods. Extensive experiments on public benchmarks show that our HOCA module effectively improves the performance of various CNN-based SR methods.

关键词： Adaptation models Conferences Superresolution Semantics Signal processing Logic gates Benchmark testing

ORTHOGONAL CONSTRAINED MINIMIZATION WITH TENSOR 2,p REGULARIZATION FOR HSI DENOISING AND DESTRIPING

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arXiv 2024年

作者： Liu, Xiaoxia Yu, Shijie Lu, Jian Chen, Xiaojun Shenzhen Key Laboratory of Advanced Machine Learning and Applications School of Mathematical Sciences Shenzhen University Shenzhen518060 China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong National Center for Applied Mathematics Shenzhen Shenzhen518055 China

Hyperspectral images (HSIs) are often contaminated by a mixture of noises such as Gaussian noise, dead lines, stripes, and so on. In this paper, we propose a novel approach for HSI denoising and destriping, called MLTL2p, which consists of an orthogonal constrained minimization model and an iterative algorithm with convergence guarantees. The model of the proposed MLTL2p approach is built based on a new sparsity-enhanced Multi-scale Low-rank Tensor regularization and a tensor 2,p norm with p ∈ (0, 1). The multi-scale low-rank regularization for HSI denoising utilizes the global and local spectral correlation as well as the spatial nonlocal self-similarity priors of HSIs. The corresponding low-rank constraints are formulated based on independent higher-order singular value decomposition with sparsity enhancement on its core tensor to prompt more low-rankness. The tensor 2,p norm for HSI destriping is extended from the matrix 2,p norm. A proximal block coordinate descent algorithm is proposed in the MLTL2p approach to solve the resulting nonconvex nonsmooth minimization with orthogonal constraints. We show any accumulation point of the sequence generated by the proposed algorithm converges to a first-order stationary point, which is defined using three equalities of substationarity, symmetry, and feasibility for orthogonal constraints. In the numerical experiments, we compare the proposed method with state-of-the-art methods including a deep learning based method, and test the methods on both simulated and real HSI datasets. Our proposed MLTL2p method demonstrates outperformance in terms of metrics such as mean peak signal-to-noise ratio as well as visual *** Codes 68U10, 90C26, 15A18, 65F22 Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

RIDNet: Recursive Information Distillation Network for Color Image Denoising

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RIDNet: Recursive Information Distillation Network for Color...

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Shengkai Zhuo Zhi Jin Wenbin Zou Xia Li Shenzhen University China College of Electronics and Information Engineering Shenzhen Key Laboratory of Advanced Machine Learning and Applications Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University School of Intelligent Systems Engineering Sun Yat-sen University Sun Yat-sen University China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Color image denoising is more challenging in effectiveness when compared with the grayscale one. Most existing methods play a certain role in efficiency or flexibility, but lack robustness to handle various noise levels, especially the severe noise. This keeps them away from being practically applied to color image denoising. To address this issue, we propose a robust CNN based denoiser, namely Recursive Information Distillation Network (RIDNet), to handle the denoising task at high noise levels. The proposed RIDNet simultaneously keeps the efficiency and flexibility by introducing the information distillation module and merging a tunable noise level map as the input, respectively. Experiment results on Additive White Gaussian Noise (AWGN) images demonstrate that our method outperforms most of the state-of-the-art color image denoisers.

关键词： Noise reduction Noise level Color Training AWGN Noise measurement Task analysis

Geometric Edge Convolution for Rigid Transformation Invariant Features in 3d Point Clouds

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SSRN

SSRN 2024年

作者： Bello, Saifullahi Aminu Alfasly, Saghir Mao, Jiawei Lu, Jian Li, Lin Xu, Chen Zou, Yuru Shenzhen Key Laboratory of Advanced Machine Learning and Applications College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Shenzhen518055 China Pazhou Lab Guangzhou510335 China School of Electronic Engineering Xidian University Xi’an710071 China

Extracting rigid transformation invariant features is still a challenge on 3D point clouds because rigid transformation changes the point coordinates, and relying on the point coordinates, most existing deep learning models fail. This paper addresses this challenge by proposing geometric edge convolution (GEConv). To facilitate rigid transformation invariant feature extraction, GEConv uses geometric features to construct a local graph and use shared multi-layer perceptron (MLP) on the graph edges to extract deep features. By stacking GEConv, we construct a deep model called GEConvNet that hierarchically extracts different level rigid transformation invariant features for various point cloud tasks. To prove the effectiveness of GEConv, we provide experiments on four point cloud processing tasks;classification, parts segmentation, semantic segmentation, and point set registration. In the classification and segmentation tasks, GEConvNet performs better on robustness to rotation compared to existing models. Furthermore, our feature-matching-based approach for point set registration demonstrates strong performance, providing further evidence of GEConv's ability to handle rotation and translation effectively. Our code will be made available on http://***/saifabel/GEConv © 2024, The Authors. All rights reserved.

关键词： Feature extraction

Video salient object detection using a virtual border and guided filter

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Pattern Recognition 2020年 97卷

作者： Wang, Qiong Zhang, Lu Zou, Wenbin Kpalma, Kidiyo College of Computer Science and Technology Zhejiang University of Technology No.288 Road Liuhe Hangzhou310023 China - UMR 6164 RennesF-35000 France College of Electronic and Information Engineering Shenzhen Key Laboratory of Advanced Machine Learning and Applications Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

In this paper, we present a novel method for salient object detection in videos. Salient object detection methods based on background prior may miss salient region when the salient object touches the frame borders. To solve this problem, we propose to detect the whole salient object via the adjunction of virtual borders. A guided filter is then applied on the temporal output to integrate the spatial edge information for a better detection of the salient object edges. At last, a global spatio-temporal saliency map is obtained by combining the spatial saliency map and the temporal saliency map together according to the entropy. The proposed method is assessed on three popular datasets (Fukuchi, FBMS and VOS) and compared to several state-of-the-art methods. The experimental results show that the proposed approach outperforms the tested methods. © 2019

关键词： Object detection

Convergence Rates of Subgradient Methods for Quasi-convex Optimization Problems

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arXiv 2019年

作者： Hu, Yaohua Li, Jiawen Yu, Carisa Kwok Wai Shenzhen Key Laboratory of Advanced Machine Learning and Applications College of Mathematics and Statistics Shenzhen University Shenzhen518060 China College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Department of Mathematics and Statistics Hang Seng University of Hong Kong Hong Kong

Quasi-convex optimization acts a pivotal part in many fields including economics and finance;the subgradient method is an effective iterative algorithm for solving large-scale quasi-convex optimization problems. In this paper, we investigate the iteration complexity and convergence rates of various subgradient methods for solving quasi-convex optimization in a unified framework. In particular, we consider a sequence satisfying a general (inexact) basic inequality, and investigate the global convergence theorem and the iteration complexity when using the constant, diminishing or dynamic stepsize rules. More important, we establish the linear (or sublinear) convergence rates of the sequence under an additional assumption of weak sharp minima of Hölderian order and upper bounded noise. These convergence theorems are applied to establish the iteration complexity and convergence rates of several subgradient methods, including the standard/inexact/conditional subgradient methods, for solving quasi-convex optimization problems under the assumptions of the Hölder condition and/or the weak sharp minima of Hölderian *** Codes 65K05, 90C26 Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization