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MULTI-VIEW LEARNING

Multi-View Learning is a machine learning framework where data are represented by multiple distinct feature groups, and each feature group is referred to as a particular view.

Source: Dissimilarity-based representation for radiomics applications

Papers

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Showing 1–50 of 256 papers

TitleStatusHype
Reliable Conflictive Multi-View LearningCode2
Trusted Multi-View Classification with Dynamic Evidential FusionCode2
Robust Variational Contrastive Learning for Partially View-unaligned ClusteringCode1
Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image SegmentationCode1
Dual Adversarial Domain AdaptationCode1
COMPLETER: Incomplete Multi-view Clustering via Contrastive PredictionCode1
Tensor Canonical Correlation Analysis for Multi-view Dimension ReductionCode1
Multi-View Learning with Context-Guided Receptance for Image DenoisingCode1
Heterogeneous Graph Contrastive Multi-view LearningCode1
Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing ApplicationsCode1
A Clustering-guided Contrastive Fusion for Multi-view Representation LearningCode1
Deep Multi-View Learning via Task-Optimal CCACode1
Deep Tensor CCA for Multi-view LearningCode1
CPM-Nets: Cross Partial Multi-View NetworksCode1
A Comparative Assessment of Multi-view fusion learning for Crop ClassificationCode1
Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPRCode1
Shared Independent Component Analysis for Multi-Subject NeuroimagingCode1
SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWBCode1
TSCMamba: Mamba Meets Multi-View Learning for Time Series ClassificationCode1
Variational Distillation for Multi-View LearningCode1
Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-IdentificationCode1
Co-mining: Self-Supervised Learning for Sparsely Annotated Object DetectionCode1
Trusted Multi-View ClassificationCode1
Dual Contrastive Prediction for Incomplete Multi-view Representation LearningCode1
Learning Autoencoders with Relational RegularizationCode1
Siamese DETRCode1
Localized Sparse Incomplete Multi-view ClusteringCode1
LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal DataCode1
Molecule Generation for Target Protein Binding with Hierarchical Consistency Diffusion ModelCode1
ConsRec: Learning Consensus Behind Interactions for Group RecommendationCode1
A Survey on Multi-Task LearningCode0
Missing Data as Augmentation in the Earth Observation Domain: A Multi-View Learning ApproachCode0
ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity RecognitionCode0
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity TypingCode0
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing DataCode0
Integrative Multi-View Reduced-Rank Regression: Bridging Group-Sparse and Low-Rank ModelsCode0
Impact Assessment of Missing Data in Model Predictions for Earth Observation ApplicationsCode0
Increasing the Robustness of Model Predictions to Missing Sensors in Earth ObservationCode0
Learning Dual Retrieval Module for Semi-supervised Relation ExtractionCode0
Multi-View Broad Learning System for Primate Oculomotor Decision DecodingCode0
Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack DetectionCode0
Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised LearningCode0
Bundle Recommendation with Item-level Causation-enhanced Multi-view LearningCode0
EIT: Enhanced Interactive TransformerCode0
Fine-Tuning Language Models with Reward Learning on PolicyCode0
Explainable Multi-View Deep Networks Methodology for Experimental PhysicsCode0
Dual Memory Neural Computer for Asynchronous Two-view Sequential LearningCode0
Balanced Multi-view ClusteringCode0
Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield PredictionCode0
Dynamic Evidence Decoupling for Trusted Multi-view LearningCode0
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