Model architecture refinement is a challenging task in deep learning research fields such as remote photoplethysmography (rPPG). One architectural consideration, the depth of the model, can have significant consequences on the resulting performance. In rPPG models that are overprovisioned with more layers than necessary, redundancies exist, the removal of which can result in faster training and reduced computational load at inference time. With too few layers the models may exhibit sub-optimal error rates. We apply Centered Kernel Alignment (CKA) to an array of rPPG architectures of differing depths, demonstrating that shallower models do not learn the same representations as deeper models, and that after a certain depth, redundant layers are added without significantly increased functionality. An empirical study confirms these findings and shows how this method could be used to refine rPPG architectures.

翻译：模型架构优化是远程光电容积描记（rPPG）等深度学习研究领域中的一项挑战性任务。模型深度这一架构关键因素，会对最终性能产生重大影响。在层数过多的过度配置的rPPG模型中，存在冗余层，移除这些冗余可加速训练过程并降低推理时的计算负载。若层数过少，模型则可能表现出欠优的错误率。我们将中心核对齐（CKA）应用于一组不同深度的rPPG架构，结果表明：浅层模型无法学习到与深层模型相同的表征，且在达到特定深度后，新增的冗余层不会显著增强模型功能。一项实证研究证实了这些发现，并展示了如何利用该方法优化rPPG架构。