mlsanityData to 7 Oct 2026

Reliable Conflictive Multi-View Learning

arXiv 2402.16897 ↗First posted 24 Feb 2024cs.LG cs.AIcited by 45

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Abstract (arXiv)

Multi-view learning aims to combine multiple features to achieve more comprehensive descriptions of data. Most previous works assume that multiple views are strictly aligned. However, real-world multi-view data may contain low-quality conflictive instances, which show conflictive information in different views. Previous methods for this problem mainly focus on eliminating the conflictive data instances by removing them or replacing conflictive views. Nevertheless, real-world applications usually require making decisions for conflictive instances rather than only eliminating them. To solve this, we point out a new Reliable Conflictive Multi-view Learning (RCML) problem, which requires the model to provide decision results and attached reliabilities for conflictive multi-view data. We develop an Evidential Conflictive Multi-view Learning (ECML) method for this problem. ECML first learns view-specific evidence, which could be termed as the amount of support to each category collected from data. Then, we can construct view-specific opinions consisting of decision results and reliability. In the multi-view fusion stage, we propose a conflictive opinion aggregation strategy and theoretically prove this strategy can exactly model the relation of multi-view common and view-specific reliabilities. Experiments performed on 6 datasets verify the effectiveness of ECML.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). Builds on 14 of its 36 references that are in the map. First posted on arXiv 24 Feb 2024.

Builds on

14 of 36 references.

References · 14

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  3. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 728
  4. 2022Trusted Multi-View Classification with Dynamic Evidential Fusioncited by 167
  5. 2016On Deep Multi-View Representation Learning: Objectives and Optimizationcited by 162
  6. 2021Trusted Multi-View Classificationcited by 94
  7. 2023Provable Dynamic Fusion for Low-Quality Multimodal Datacited by 81
  8. 2020Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentationcited by 61
  9. 2017Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generationcited by 59
  10. 2022A Survey on Incomplete Multi-view Clusteringcited by 31
  11. 2022Uncertainty-Aware Multi-View Representation Learningcited by 29
  12. 2023Unpaired Multi-View Graph Clustering with Cross-View Structure Matchingcited by 19
2 more
  1. 2022Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picturecited by 10
  2. 2022MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendationcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 13; 2025: 14; 2026: 18.

45 papers.

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  1. 2025Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Priorcited by 28
  2. 2024A Comprehensive Survey on Evidential Deep Learning and Its Applicationscited by 25
  3. 2024CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot Learningcited by 12
  4. 2025Uncertainty Estimation by Flexible Evidential Deep Learningcited by 10
  5. 2024Trusted Multi-view Learning under Noisy Supervisioncited by 8
  6. 2025Cooperation of Experts: Fusing Heterogeneous Information with Large Margincited by 6
  7. 2024Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuitscited by 5
  8. 2024LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal Datacited by 5
  9. 2024Evidential Deep Partial Multi-View Classification With Discount Fusioncited by 5
  10. 2024Navigating Conflicting Views: Harnessing Trust for Learningcited by 4
  11. 2024Dynamic Evidence Decoupling for Trusted Multi-view Learningcited by 4
  12. 2024Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamicscited by 4
33 more
  1. 2024Multimodal Learning with Uncertainty Quantification based on Discounted Belief Fusioncited by 4
  2. 2026Life After Benchmark Saturation: A Case Study of CORE-Benchcited by 3
  3. 2025Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learningcited by 2
  4. 2025Interpretable Generative and Discriminative Learning for Multimodal and Incomplete Clinical Datacited by 2
  5. 2026Context-specific Credibility-aware Multimodal Fusion with Conditional Probabilistic Circuitscited by 2
  6. 2026Revisiting Uncertainty: On Evidential Learning for Partially Relevant Video Retrievalcited by 2
  7. 2024Devil in the Tail: A Multi-Modal Framework for Drug-Drug Interaction Prediction in Long Tail Distinctioncited by 1
  8. 2024Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion Aggregationcited by 1
  9. 2025Multi-View Factorizing and Disentangling: A Novel Framework for Incomplete Multi-View Multi-Label Classificationcited by 1
  10. 2025Reliable Disentanglement Multi-view Learning Against View Adversarial Attackscited by 1
  11. 2025Enhancing Multi-view Open-set Learning via Ambiguity Uncertainty Calibration and View-wise Debiasingcited by 1
  12. 2025Towards Comprehensive Information-theoretic Multi-view Learningcited by 1
  13. 2025Bayesian-based Online Label Shift Estimation with Dynamic Dirichlet Priorscited by 1
  14. 2026CaReFlow: Cyclic Adaptive Rectified Flow for Multimodal Fusioncited by 1
  15. 2026Uncertainty-Aware Deepfake Detection via Multi-View Structural Learningcited by 1
  16. 2024Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemblecited by 0
  17. 2025Post-Hoc Split-Point Self-Consistency Verification for Efficient, Unified Quantification of Aleatoric and Epistemic Uncertainty in Deep Learningcited by 0
  18. 2025A Hybrid Framework Bridging CNN and ViT based on Theory of Evidence for Diabetic Retinopathy Gradingcited by 0
  19. 2025Structure-Aware Prototype Guided Trusted Multi-View Classificationcited by 0
  20. 2025Enhancing Semi-Supervised Multi-View Graph Convolutional Networks via Supervised Contrastive Learning and Self-Trainingcited by 0
  21. 2026Explaining Synergistic Effects in Social Recommendationscited by 0
  22. 2026Bayesian Integration of Nonlinear Incomplete Clinical Datacited by 0
  23. 2026BD-Merging: Bias-Aware Dynamic Model Merging with Evidence-Guided Contrastive Learningcited by 0
  24. 2026AdaMuS: Adaptive Multi-view Sparsity Learning for Dimensionally Unbalanced Datacited by 0
  25. 2026Multi-view Graph Convolutional Network with Fully Leveraging Consistency via Granular-ball-based Topology Construction, Feature Enhancement and Interactive Fusioncited by 0
  26. 2026Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibrationcited by 0
  27. 2026BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregationcited by 0
  28. 2026Robust Fuzzy Multi-view Learning under View Conflictcited by 0
  29. 2026Robust Multi-View Classification under Noisy Supervision via Global Anchor Consensuscited by 0
  30. 2026Pretraining Reusable Inference Across Views with Synthetic Task Priorscited by 0
  31. 2026Absorbing Gradient Conflicts: Modeling Semantic Variance via Kent Distributions for Cross-Modal Hashingcited by 0
  32. 2026Multi-View Trust Evaluation for Collaborator Selection via Evidential Deep Learningcited by 0
  33. 2026Lucid Dreaming for World Models: Learning to Doubt Imagination and Decide by Trustcited by 0