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Found 15 papers, 5 papers with code
Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering
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.
Federated Deep Multi-View Clustering with Global Self-Supervision
Second, the storage and usage of data from multiple clients in a distributed environment can lead to incompleteness of multi-view data.
Deep Multi-View Subspace Clustering with Anchor Graph
To significantly reduce the complexity, we construct an anchor graph with small size for each view.
Self-Paced Neutral Expression-Disentangled Learning for Facial Expression Recognition
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.
Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey
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.
Variational Graph Generator for Multi-View Graph Clustering
The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views.
Deep Clustering: A Comprehensive Survey
Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering.
Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs
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.
EEGDnet: Fusing Non-Local and Local Self-Similarity for 1-D EEG Signal Denoising with 2-D Transformer
To address this issue, we propose a novel 1-D EEG signal denoising network with 2-D transformer, namely EEGDnet.
Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering
The prior of view-common variable obeys approximately discrete Gumbel Softmax distribution, which is introduced to extract the common cluster factor of multiple views.
GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT Denoising
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.
Non-Linear Fusion for Self-Paced Multi-View Clustering
In NSMVC, we directly assign different exponents to different views according to their qualities.
Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection
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.
Self-supervised Discriminative Feature Learning for Deep Multi-view Clustering
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.
Multi-graph Fusion for Multi-view Spectral Clustering
Most existing methods don't pay attention to the quality of the graphs and perform graph learning and spectral clustering separately.
