Search Results for author: Yazhou Ren

Found 32 papers, 13 papers with code

S^2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering

1 code implementation14 Mar 2024 Zhen Long, Qiyuan Wang, Yazhou Ren, Yipeng Liu, Ce Zhu

Specifically, we first construct the embedding feature tensor by stacking the embedding features of different views into a tensor and rotating it.

Clustering Graph Similarity

Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering

no code implementations5 Jan 2024 Zichen Wen, Yawen Ling, Yazhou Ren, Tianyi Wu, Jianpeng Chen, Xiaorong Pu, Zhifeng Hao, Lifang He

Then we design an adaptive hybrid graph filter that is related to the homophily degree, which learns the node embedding based on the graph joint aggregation matrix.

Clustering Graph Clustering

Federated Deep Multi-View Clustering with Global Self-Supervision

no code implementations24 Sep 2023 Xinyue Chen, Jie Xu, Yazhou Ren, Xiaorong Pu, Ce Zhu, Xiaofeng Zhu, Zhifeng Hao, Lifang He

Second, the storage and usage of data from multiple clients in a distributed environment can lead to incompleteness of multi-view data.

Clustering

Multi-view MERA Subspace Clustering

1 code implementation16 May 2023 Zhen Long, Ce Zhu, Jie Chen, Zihan Li, Yazhou Ren, Yipeng Liu

Benefiting from multiple interactions among orthogonal/semi-orthogonal (low-rank) factors, the low-rank MERA has a strong representation power to capture the complex inter/intra-view information in the self-representation tensor.

Clustering Multi-view Subspace Clustering

Deep Multi-View Subspace Clustering with Anchor Graph

1 code implementation11 May 2023 Chenhang Cui, Yazhou Ren, Jingyu Pu, Xiaorong Pu, Lifang He

To significantly reduce the complexity, we construct an anchor graph with small size for each view.

Clustering Contrastive Learning +1

Self-Paced Neutral Expression-Disentangled Learning for Facial Expression Recognition

no code implementations21 Mar 2023 Zhenqian Wu, Xiaoyuan Li, Yazhou Ren, Xiaorong Pu, Xiaofeng Zhu, Lifang He

In order to better learn these neutral expression-disentangled features (NDFs) and to alleviate the non-convex optimization problem, a self-paced learning (SPL) strategy based on NDFs is proposed in the training stage.

Facial Expression Recognition

Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey

no code implementations13 Feb 2023 Song Wu, Yazhou Ren, Aodi Yang, Xinyue Chen, Xiaorong Pu, Jing He, Liqiang Nie, Philip S. Yu

In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis.

COVID-19 Diagnosis Image Classification

MHCN: A Hyperbolic Neural Network Model for Multi-view Hierarchical Clustering

no code implementations ICCV 2023 Fangfei Lin, Bing Bai, Yiwen Guo, Hao Chen, Yazhou Ren, Zenglin Xu

Multi-view hierarchical clustering (MCHC) plays a pivotal role in comprehending the structures within multi-view data, which hinges on the skillful interaction between hierarchical feature learning and comprehensive representation learning across multiple views.

Clustering MULTI-VIEW LEARNING +1

Variational Graph Generator for Multi-View Graph Clustering

no code implementations13 Oct 2022 Jianpeng Chen, Yawen Ling, Jie Xu, Yazhou Ren, Shudong Huang, Xiaorong Pu, Zhifeng Hao, Philip S. Yu, Lifang He

The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views.

Clustering Graph Clustering

Deep Clustering: A Comprehensive Survey

no code implementations9 Oct 2022 Yazhou Ren, Jingyu Pu, Zhimeng Yang, Jie Xu, Guofeng Li, Xiaorong Pu, Philip S. Yu, Lifang He

Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering.

Clustering Deep Clustering

Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs

no code implementations8 May 2022 Zongmo Huang, Yazhou Ren, Xiaorong Pu, Lifang He

To address this issue, in this paper we propose Deep Embedded Multi-view Clustering via Jointly Learning Latent Representations and Graphs (DMVCJ), which utilizes the latent graphs to promote the performance of deep embedded MVC models from two aspects.

Clustering Representation Learning

Contrastive Multi-view Hyperbolic Hierarchical Clustering

no code implementations5 May 2022 Fangfei Lin, Bing Bai, Kun Bai, Yazhou Ren, Peng Zhao, Zenglin Xu

Then, we embed the representations into a hyperbolic space and optimize the hyperbolic embeddings via a continuous relaxation of hierarchical clustering loss.

Clustering

Self-Paced Deep Regression Forests with Consideration of Ranking Fairness

1 code implementation13 Dec 2021 Lili Pan, Mingming Meng, Yazhou Ren, Yali Zheng, Zenglin Xu

To answer this question, this paper proposes a new SPL method: easy and underrepresented examples first, for learning DDMs.

Age Estimation Fairness +3

Multi-level Feature Learning for Contrastive Multi-view Clustering

1 code implementation CVPR 2022 Jie Xu, Huayi Tang, Yazhou Ren, Liang Peng, Xiaofeng Zhu, Lifang He

Our method learns different levels of features from the raw features, including low-level features, high-level features, and semantic labels/features in a fusion-free manner, so that it can effectively achieve the reconstruction objective and the consistency objectives in different feature spaces.

Clustering Contrastive Learning

Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering

no code implementations ICCV 2021 Jie Xu, Yazhou Ren, Huayi Tang, Xiaorong Pu, Xiaofeng Zhu, Ming Zeng, Lifang He

The prior of view-common variable obeys approximately discrete Gumbel Softmax distribution, which is introduced to extract the common cluster factor of multiple views.

Clustering

Customizing Graph Neural Networks using Path Reweighting

2 code implementations21 Jun 2021 Jianpeng Chen, Yujing Wang, Ming Zeng, Zongyi Xiang, Bitan Hou, Yunhai Tong, Ole J. Mengshoel, Yazhou Ren

Specifically, the proposed CustomGNN can automatically learn the high-level semantics for specific downstream tasks to highlight semantically relevant paths as well to filter out task-irrelevant noises in a graph.

Data Augmentation Graph Attention +1

GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT Denoising

3 code implementations15 May 2021 Kecheng Chen, Jiayu Sun, Jiang Shen, Jixiang Luo, Xinyu Zhang, Xuelin Pan, Dongsheng Wu, Yue Zhao, Miguel Bento, Yazhou Ren, Xiaorong Pu

To address this issue, we propose a novel graph convolutional network-based LDCT denoising model, namely GCN-MIF, to explicitly perform multi-information fusion for denoising purpose.

Denoising

Non-Linear Fusion for Self-Paced Multi-View Clustering

no code implementations19 Apr 2021 Zongmo Huang, Yazhou Ren, Xiaorong Pu, Lifang He

In NSMVC, we directly assign different exponents to different views according to their qualities.

Clustering

Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection

no code implementations18 Apr 2021 Kecheng Chen, Kun Long, Yazhou Ren, Jiayu Sun, Xiaorong Pu

To this end, we propose a play-and-plug medical image denoising framework, namely Lesion-Inspired Denoising Network (LIDnet), to collaboratively improve both denoising performance and detection accuracy of denoised medical images.

Image Denoising Lesion Detection +1

Self-supervised Discriminative Feature Learning for Deep Multi-view Clustering

1 code implementation28 Mar 2021 Jie Xu, Yazhou Ren, Huayi Tang, Zhimeng Yang, Lili Pan, Yang Yang, Xiaorong Pu

To leverage the multi-view complementary information, we concatenate all views' embedded features to form the global features, which can overcome the negative impact of some views' unclear clustering structures.

Clustering

Deep Embedded Multi-view Clustering with Collaborative Training

1 code implementation26 Jul 2020 Jie Xu, Yazhou Ren, Guofeng Li, Lili Pan, Ce Zhu, Zenglin Xu

Firstly, the embedded representations of multiple views are learned individually by deep autoencoders.

Clustering

Self-Paced Deep Regression Forests with Consideration on Underrepresented Examples

no code implementations ECCV 2020 Lili Pan, Shijie Ai, Yazhou Ren, Zenglin Xu

Deep discriminative models (e. g. deep regression forests, deep neural decision forests) have achieved remarkable success recently to solve problems such as facial age estimation and head pose estimation.

Age Estimation Fairness +2

Self-Paced Deep Regression Forests for Facial Age Estimation

no code implementations8 Oct 2019 Shijie Ai, Lili Pan, Yazhou Ren

Facial age estimation is an important and challenging problem in computer vision.

Age Estimation MORPH +1

Ranking-based Deep Cross-modal Hashing

no code implementations11 May 2019 Xuanwu Liu, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Yazhou Ren, Maozu Guo

Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions.

Cross-Modal Retrieval Retrieval

Latent Dirichlet Allocation in Generative Adversarial Networks

no code implementations17 Dec 2018 Lili Pan, Shen Cheng, Jian Liu, Yazhou Ren, Zenglin Xu

We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs).

Image Generation multimodal generation +1

Deep Density-based Image Clustering

1 code implementation11 Dec 2018 Yazhou Ren, Ni Wang, Mingxia Li, Zenglin Xu

Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications.

Clustering Deep Clustering +1

Self-Paced Multi-Task Clustering

1 code implementation24 Aug 2018 Yazhou Ren, Xiaofan Que, Dezhong Yao, Zenglin Xu

Despite the success of traditional MTC models, they are either easy to stuck into local optima, or sensitive to outliers and noisy data.

Clustering

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