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English(EN) Parameter-efficient Dual-encoder Architecture with Differentiable Choquet Integral Fusion for Underwater Acoustic Classification

新的双编码器架构改进水下声学分类

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的双编码器神经网络架构，用于水下声学分类，可同时处理波形和频谱图数据。该方法利用参数高效微调和可微分Choquet积分进行融合，旨在提高准确性和可解释性。该方法在基准数据集上显示出改进的分类性能，同时降低了计算成本和过拟合的风险。 AI

影响 在分类任务中引入了一种新颖的多模态数据融合机制，有望提高专业领域的性能。

排序理由 该集群包含一篇详细介绍特定分类任务新方法的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Amirmohammad Mohammadi, Joshua Peeples, Alexandra Van Dine ·

    Parameter-efficient Dual-encoder Architecture with Differentiable Choquet Integral Fusion for Underwater Acoustic Classification

    arXiv:2606.02341v1 Announce Type: cross Abstract: Underwater acoustic classification has a wide array of oceanic applications, but faces challenges due to an increasingly complex acoustic environment. Waveform and spectrogram representations have been primarily used as acoustic d…

  2. arXiv cs.LG TIER_1 English(EN) · Alexandra Van Dine ·

    Parameter-efficient Dual-encoder Architecture with Differentiable Choquet Integral Fusion for Underwater Acoustic Classification

    Underwater acoustic classification has a wide array of oceanic applications, but faces challenges due to an increasingly complex acoustic environment. Waveform and spectrogram representations have been primarily used as acoustic data features for classification tasks in this doma…

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