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Cross-Modal Multivariate Pattern Analysis
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MOAL: Multi-view Out-of-distribution Awareness Learning.

Xuzheng Wang1, Zihan Fang1, Shide Du1

  • 1College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China; Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350108, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 1, 2025
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Summary

This study introduces MOLA, a novel method for multi-view learning that effectively identifies out-of-distribution data. MOLA enhances perception of typical features and minimizes entropy for anomalous instances, improving accuracy.

Keywords:
Anomaly detectionDeep learningMulti-view learningOut-of-distribution learning

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Area of Science:

  • Machine Learning
  • Computer Vision
  • Data Science

Background:

  • Multi-view learning leverages multiple data sources for improved performance.
  • Current methods struggle with identifying out-of-distribution (OOD) data, limiting real-world applicability.
  • Effective OOD detection is crucial for robust AI systems.

Purpose of the Study:

  • To develop a novel method for enhancing out-of-distribution data perception in multi-view learning scenarios.
  • To improve the ability of multi-view models to distinguish between in-distribution and out-of-distribution instances.
  • To address the limitations of existing multi-view learning approaches in handling novel or anomalous data.

Main Methods:

  • Developed sub-view complementarity representation learning and multi-view consistency fusion layers based on consistency and complementarity principles.
  • Introduced a specialized multi-view training loss function.
  • Incorporated an agent mechanism specifically designed for out-of-distribution scenarios.

Main Results:

  • The proposed method, MOLA, significantly enhances the perception of intra-distribution features.
  • MOLA effectively minimizes the entropy associated with out-of-distribution instances.
  • Experimental results demonstrated consistent performance improvements over baselines, with an average accuracy increase exceeding 5% on simulated OOD datasets.

Conclusions:

  • MOLA offers a robust solution for out-of-distribution detection in multi-view learning.
  • The approach effectively balances intra-distribution feature representation enhancement with OOD instance identification.
  • MOLA represents a significant advancement in developing more reliable and adaptable multi-view learning systems.