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English(EN) Applicability of memorization indicators for early spotting of overfitting while recalibrating sEMG-decoders on low sample sizes

记忆指标可能有助于检测表面肌电信号深度学习中的过拟合

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员探索了使用记忆指标来检测表面肌电信号（sEMG）解码器深度学习模型中的过拟合，特别是在受试者特定重新校准的样本量有限的情况下。在这些低样本场景中，诸如验证性能和提前停止等传统方法难以应用。研究表明，修正线性单元（ReLU）激活率的变化可以指示微调过程中学习不成功，为早期识别过拟合提供了一个有前景的工具。 AI

影响 这项研究通过改进对校准过程中过拟合的检测，可能带来更可靠的表面肌电信号解码器。

排序理由 该集群包含一篇研究论文，详细介绍了一种检测深度学习模型过拟合的新方法。

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记忆指标可能有助于检测表面肌电信号深度学习中的过拟合

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该集群包含一篇研究论文，详细介绍了一种检测深度学习模型过拟合的新方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Stephan J. Lehmler, Tobias Glasmachers, Ioannis Iossifidis ·

    记忆指标在低样本量下重新校准sEMG解码器时用于早期识别过拟合的适用性

    arXiv:2606.27855v1 Announce Type: cross Abstract: Deep learning models for surface electromyography (sEMG) can benefit substantially from subject-specific (re-)calibration, since no sufficiently large and diverse datasets are available to train fully generic decoders. However, fo…

  2. arXiv cs.AI TIER_1 English(EN) · Ioannis Iossifidis ·

    记忆指标在低样本量下重新校准sEMG解码器时用于早期识别过拟合的适用性

    Deep learning models for surface electromyography (sEMG) can benefit substantially from subject-specific (re-)calibration, since no sufficiently large and diverse datasets are available to train fully generic decoders. However, for user acceptance, the number of repetitions that …

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