Distribution shift poses a significant challenge in machine learning, particularly in biomedical applications using data collected across different subjects, institutions, and recording devices, such as sleep data. While existing normalization layers, BatchNorm, LayerNorm and InstanceNorm, help mitigate distribution shifts, when applied over the time dimension they ignore the dependencies and auto-correlation inherent to the vector coefficients they normalize. In this paper, we propose PSDNorm that leverages Monge mapping and temporal context to normalize feature maps in deep learning models for signals. Evaluations with architectures based on U-Net or transformer backbones trained on 10K subjects across 10 datasets, show that PSDNorm achieves state-of-the-art performance on unseen left-out datasets while being more robust to data scarcity.

翻译：分布偏移在机器学习中构成了一项重大挑战，尤其在涉及跨不同受试者、机构和记录设备收集数据的生物医学应用（如睡眠数据）中。现有的归一化层，如BatchNorm、LayerNorm和InstanceNorm，虽然有助于缓解分布偏移，但当它们应用于时间维度时，会忽略其所归一化的向量系数固有的依赖性和自相关性。本文提出PSDNorm，它利用Monge映射和时间上下文来对信号深度学习模型中的特征图进行归一化。基于U-Net或Transformer架构、在10个数据集的10K名受试者数据上训练的模型评估表明，PSDNorm在未见过的留出数据集上实现了最先进的性能，同时对数据稀缺性具有更强的鲁棒性。