English(EN) APRIL: Ambiguity-Aware Expert Routing for Respiratory Sound Domain-Incremental Learning

新的APRIL框架通过域增量学习增强呼吸音分类

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

研究人员开发了APRIL，一种用于呼吸音分类域增量学习的新颖框架。这种无需示例的方法利用共享的BEATs骨干和低秩专家来适应新域，而不会损害先前获得的知识。APRIL采用多级原型路由器，通过整合域路由后验与分类置信度来动态选择最佳专家，并通过匹配歧义进行自适应加权。在ICBHI和SPRSound数据集上进行测试，APRIL持续优于其他持续学习方法，展示了在最小遗忘的情况下稳健的知识保留能力。 AI

影响该框架通过适应不断变化的数据而无需回放敏感的历史信息，为持续的AI驱动的医疗保健监控提供了一种隐私保护的解决方案。

排序理由研究论文，详细介绍了一种新的域增量学习框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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Newsworthiness bucket: Tool; 研究论文，详细介绍了一种新的域增量学习框架。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Xinhong Li, Weiping Tu · 2026-10-21 04:00

APRIL：呼吸音域增量学习中的歧义感知专家路由

arXiv:2610.27942v1 Announce Type: cross Abstract: Automated respiratory sound classification enables continuous, non-invasive respiratory assessment, yet long-term deployment remains challenging due to domain shifts from evolving recording devices and acquisition periods. When pr…

本文如何被排名

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APRIL：呼吸音域增量学习中的歧义感知专家路由

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