DUAL: Dynamic Uncertainty-Aware Learning

• URL: http://arxiv.org/abs/2506.03158v1
• Date: Wed, 21 May 2025 18:50:15 GMT
• Title: DUAL: Dynamic Uncertainty-Aware Learning
• Authors: Jiahao Qin, Bei Peng, Feng Liu, Guangliang Cheng, Lu Zong,
• Abstract summary: We propose DynamicUncertainty-Aware Learning (DUAL), a unified framework that effectively handles feature uncertainty in both single-modal and multi-modal scenarios.<n>DUAL introduces three key innovations: Dynamic Feature Uncertainty Modeling, Adaptive Distribution-Aware Modulation, and Uncertainty-aware Cross-Modal Relationship.
• Score: 19.100858792977807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning models frequently encounter feature uncertainty in diverse learning scenarios, significantly impacting their performance and reliability. This challenge is particularly complex in multi-modal scenarios, where models must integrate information from different sources with inherent uncertainties. We propose Dynamic Uncertainty-Aware Learning (DUAL), a unified framework that effectively handles feature uncertainty in both single-modal and multi-modal scenarios. DUAL introduces three key innovations: Dynamic Feature Uncertainty Modeling, which continuously refines uncertainty estimates through joint consideration of feature characteristics and learning dynamics; Adaptive Distribution-Aware Modulation, which maintains balanced feature distributions through dynamic sample influence adjustment; and Uncertainty-aware Cross-Modal Relationship Learning, which explicitly models uncertainties in cross-modal interactions. Through extensive experiments, we demonstrate DUAL's effectiveness across multiple domains: in computer vision tasks, it achieves substantial improvements of 7.1% accuracy on CIFAR-10, 6.5% accuracy on CIFAR-100, and 2.3% accuracy on Tiny-ImageNet; in multi-modal learning, it demonstrates consistent gains of 4.1% accuracy on CMU-MOSEI and 2.8% accuracy on CMU-MOSI for sentiment analysis, while achieving 1.4% accuracy improvements on MISR. The code will be available on GitHub soon.

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