提出新测试时适应框架，提升生物信号实时预测鲁棒性
New Test-Time Scenario for Biosignal: Concept and Its Approach
- 结合有监督与自监督学习，动态处理无标签与偶尔有标签数据
- 在真实场景下实现血压预测准确率显著提升，适应能力更强
- 适合医疗健康领域需持续更新的实时生物信号任务
在线测试时适应（OTTA）通过在测试阶段用无标签数据更新预训练模型来增强模型鲁棒性。在医疗领域，OTTA对从生物信号实时预测血压等任务至关重要，要求持续适应。我们提出一种新测试时场景，包含无标签样本流和偶发的有标签样本。框架融合有监督与自监督学习，采用双队列缓冲区和加权批次采样策略以平衡数据类型。实验表明，在真实条件下模型准确率与适应能力均得到显著提升。
原文摘要 · Abstract (English)
Online Test-Time Adaptation (OTTA) enhances model robustness by updating pre-trained models with unlabeled data during testing. In healthcare, OTTA is vital for real-time tasks like predicting blood pressure from biosignals, which demand continuous adaptation. We introduce a new test-time scenario with streams of unlabeled samples and occasional labeled samples. Our framework combines supervised and self-supervised learning, employing a dual-queue buffer and weighted batch sampling to balance data types. Experiments show improved accuracy and adaptability under real-world conditions.
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