Trusted Multi-View Classification with Dynamic Evidential Fusion

• URL: http://arxiv.org/abs/2204.11423v2
• Date: Wed, 27 Apr 2022 14:03:00 GMT
• Title: Trusted Multi-View Classification with Dynamic Evidential Fusion
• Authors: Zongbo Han, Changqing Zhang, Huazhu Fu, and Joey Tianyi Zhou
• Abstract summary: We propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC) TMC provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.
• Score: 73.35990456162745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing multi-view classification algorithms focus on promoting accuracy by exploiting different views, typically integrating them into common representations for follow-up tasks. Although effective, it is also crucial to ensure the reliability of both the multi-view integration and the final decision, especially for noisy, corrupted and out-of-distribution data. Dynamically assessing the trustworthiness of each view for different samples could provide reliable integration. This can be achieved through uncertainty estimation. With this in mind, we propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC), providing a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. The proposed TMC can promote classification reliability by considering evidence from each view. Specifically, we introduce the variational Dirichlet to characterize the distribution of the class probabilities, parameterized with evidence from different views and integrated with the Dempster-Shafer theory. The unified learning framework induces accurate uncertainty and accordingly endows the model with both reliability and robustness against possible noise or corruption. Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.

Related papers

• Dynamic Evidence Decoupling for Trusted Multi-view Learning [17.029245880233816]
  We propose a Consistent and Complementary-aware trusted Multi-view Learning (CCML) method to solve this problem. We first construct view opinions using evidential deep neural networks, which consist of belief mass vectors and uncertainty estimates. The results validate the effectiveness of the dynamic evidence decoupling strategy and show that CCML significantly outperforms baselines on accuracy and reliability.
  arXiv  Detail & Related papers  (2024-10-04T03:27:51Z)
• Navigating Conflicting Views: Harnessing Trust for Learning [5.4486293124577125]
  We develop a computational trust-based discounting method to enhance the existing trustworthy framework. We evaluate our method on six real-world datasets, using Top-1 Accuracy, AUC-ROC for Uncertainty-Aware Prediction, Fleiss' Kappa, and a new metric called Multi-View Agreement with Ground Truth.
  arXiv  Detail & Related papers  (2024-06-03T03:22:18Z)
• Confidence-aware multi-modality learning for eye disease screening [58.861421804458395]
  We propose a novel multi-modality evidential fusion pipeline for eye disease screening. It provides a measure of confidence for each modality and elegantly integrates the multi-modality information. Experimental results on both public and internal datasets demonstrate that our model excels in robustness.
  arXiv  Detail & Related papers  (2024-05-28T13:27:30Z)
• MERIT: Multi-view Evidential learning for Reliable and Interpretable liver fibrosis sTaging [29.542924813666698]
  We propose a new multi-view method based on evidential learning, referred to as MERIT. MERIT enables uncertainty of the predictions to enhance reliability, and employs a logic-based combination rule to improve interpretability. Results have showcased the effectiveness of the proposed MERIT, highlighting the reliability and offering both ad-hoc and post-hoc interpretability.
  arXiv  Detail & Related papers  (2024-05-05T12:52:28Z)
• ELFNet: Evidential Local-global Fusion for Stereo Matching [17.675146012208124]
  We introduce the textbfEvidential textbfLocal-global textbfFusion (ELF) framework for stereo matching. It endows both uncertainty estimation and confidence-aware fusion with trustworthy heads.
  arXiv  Detail & Related papers  (2023-08-01T15:51:04Z)
• Variational Distillation for Multi-View Learning [104.17551354374821]
  We design several variational information bottlenecks to exploit two key characteristics for multi-view representation learning. Under rigorously theoretical guarantee, our approach enables IB to grasp the intrinsic correlation between observations and semantic labels.
  arXiv  Detail & Related papers  (2022-06-20T03:09:46Z)
• Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions [91.63716984911278]
  We introduce a novel Mixture of Normal-Inverse Gamma distributions (MoNIG) algorithm, which efficiently estimates uncertainty in principle for adaptive integration of different modalities and produces a trustworthy regression result. Experimental results on both synthetic and different real-world data demonstrate the effectiveness and trustworthiness of our method on various multimodal regression tasks.
  arXiv  Detail & Related papers  (2021-11-11T14:28:12Z)
• Trusted Multi-View Classification [76.73585034192894]
  We propose a novel multi-view classification method, termed trusted multi-view classification. It provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. The proposed algorithm jointly utilizes multiple views to promote both classification reliability and robustness.
  arXiv  Detail & Related papers  (2021-02-03T13:30:26Z)
• Variational Inference for Deep Probabilistic Canonical Correlation Analysis [49.36636239154184]
  We propose a deep probabilistic multi-view model that is composed of a linear multi-view layer and deep generative networks as observation models. An efficient variational inference procedure is developed that approximates the posterior distributions of the latent probabilistic multi-view layer. A generalization to models with arbitrary number of views is also proposed.
  arXiv  Detail & Related papers  (2020-03-09T17:51:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.